jayzeng commited on
Commit
a3b07fb
1 Parent(s): d560bbb

Upload folder using huggingface_hub

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. README.md +151 -0
  2. adapter_config.json +36 -0
  3. adapter_model.bin +3 -0
  4. added_tokens.json +5 -0
  5. checkpoint-1425/README.md +202 -0
  6. checkpoint-1425/adapter_config.json +36 -0
  7. checkpoint-1425/adapter_model.safetensors +3 -0
  8. checkpoint-1425/added_tokens.json +5 -0
  9. checkpoint-1425/merges.txt +0 -0
  10. checkpoint-1425/optimizer.pt +3 -0
  11. checkpoint-1425/rng_state.pth +3 -0
  12. checkpoint-1425/scheduler.pt +3 -0
  13. checkpoint-1425/special_tokens_map.json +20 -0
  14. checkpoint-1425/tokenizer.json +0 -0
  15. checkpoint-1425/tokenizer_config.json +43 -0
  16. checkpoint-1425/trainer_state.json +0 -0
  17. checkpoint-1425/training_args.bin +3 -0
  18. checkpoint-1425/vocab.json +0 -0
  19. checkpoint-1900/README.md +202 -0
  20. checkpoint-1900/adapter_config.json +36 -0
  21. checkpoint-1900/adapter_model.safetensors +3 -0
  22. checkpoint-1900/added_tokens.json +5 -0
  23. checkpoint-1900/merges.txt +0 -0
  24. checkpoint-1900/optimizer.pt +3 -0
  25. checkpoint-1900/rng_state.pth +3 -0
  26. checkpoint-1900/scheduler.pt +3 -0
  27. checkpoint-1900/special_tokens_map.json +20 -0
  28. checkpoint-1900/tokenizer.json +0 -0
  29. checkpoint-1900/tokenizer_config.json +43 -0
  30. checkpoint-1900/trainer_state.json +0 -0
  31. checkpoint-1900/training_args.bin +3 -0
  32. checkpoint-1900/vocab.json +0 -0
  33. checkpoint-475/README.md +202 -0
  34. checkpoint-475/adapter_config.json +36 -0
  35. checkpoint-475/adapter_model.safetensors +3 -0
  36. checkpoint-475/added_tokens.json +5 -0
  37. checkpoint-475/merges.txt +0 -0
  38. checkpoint-475/optimizer.pt +3 -0
  39. checkpoint-475/rng_state.pth +3 -0
  40. checkpoint-475/scheduler.pt +3 -0
  41. checkpoint-475/special_tokens_map.json +20 -0
  42. checkpoint-475/tokenizer.json +0 -0
  43. checkpoint-475/tokenizer_config.json +43 -0
  44. checkpoint-475/trainer_state.json +3378 -0
  45. checkpoint-475/training_args.bin +3 -0
  46. checkpoint-475/vocab.json +0 -0
  47. checkpoint-950/README.md +202 -0
  48. checkpoint-950/adapter_config.json +36 -0
  49. checkpoint-950/adapter_model.safetensors +3 -0
  50. checkpoint-950/added_tokens.json +5 -0
README.md ADDED
@@ -0,0 +1,151 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: other
3
+ library_name: peft
4
+ tags:
5
+ - generated_from_trainer
6
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
7
+ model-index:
8
+ - name: out
9
+ results: []
10
+ ---
11
+
12
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
13
+ should probably proofread and complete it, then remove this comment. -->
14
+
15
+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
16
+ <details><summary>See axolotl config</summary>
17
+
18
+ axolotl version: `0.4.0`
19
+ ```yaml
20
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
21
+ trust_remote_code: true
22
+
23
+ load_in_8bit: false
24
+ load_in_4bit: true
25
+ strict: false
26
+
27
+ datasets:
28
+ - path: mhenrichsen/alpaca_2k_test
29
+ type: alpaca
30
+ dataset_prepared_path:
31
+ val_set_size: 0.05
32
+ output_dir: ./out
33
+
34
+ sequence_len: 1024 # supports up to 32k
35
+ sample_packing: false
36
+ pad_to_sequence_len: false
37
+
38
+ adapter: qlora
39
+ lora_model_dir:
40
+ lora_r: 32
41
+ lora_alpha: 16
42
+ lora_dropout: 0.05
43
+ lora_target_linear: true
44
+ lora_fan_in_fan_out:
45
+
46
+ wandb_project:
47
+ wandb_entity:
48
+ wandb_watch:
49
+ wandb_name:
50
+ wandb_log_model:
51
+
52
+ gradient_accumulation_steps: 4
53
+ micro_batch_size: 1
54
+ num_epochs: 4
55
+ optimizer: paged_adamw_8bit
56
+ lr_scheduler: cosine
57
+ learning_rate: 0.0002
58
+
59
+ train_on_inputs: false
60
+ group_by_length: false
61
+ bf16: auto
62
+ fp16:
63
+ tf32: true
64
+
65
+ gradient_checkpointing: true
66
+ gradient_checkpointing_kwargs:
67
+ use_reentrant: false
68
+ early_stopping_patience:
69
+ resume_from_checkpoint:
70
+ local_rank:
71
+ logging_steps: 1
72
+ xformers_attention:
73
+ flash_attention: true
74
+
75
+ warmup_steps: 10
76
+ evals_per_epoch: 4
77
+ saves_per_epoch: 1
78
+ debug:
79
+ deepspeed:
80
+ weight_decay: 0.0
81
+ fsdp:
82
+ fsdp_config:
83
+ special_tokens:
84
+
85
+ ```
86
+
87
+ </details><br>
88
+
89
+ # out
90
+
91
+ This model is a fine-tuned version of [Qwen/Qwen1.5-MoE-A2.7B](https://huggingface.co/Qwen/Qwen1.5-MoE-A2.7B) on the None dataset.
92
+ It achieves the following results on the evaluation set:
93
+ - Loss: 1.2553
94
+
95
+ ## Model description
96
+
97
+ More information needed
98
+
99
+ ## Intended uses & limitations
100
+
101
+ More information needed
102
+
103
+ ## Training and evaluation data
104
+
105
+ More information needed
106
+
107
+ ## Training procedure
108
+
109
+ ### Training hyperparameters
110
+
111
+ The following hyperparameters were used during training:
112
+ - learning_rate: 0.0002
113
+ - train_batch_size: 1
114
+ - eval_batch_size: 1
115
+ - seed: 42
116
+ - gradient_accumulation_steps: 4
117
+ - total_train_batch_size: 4
118
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
119
+ - lr_scheduler_type: cosine
120
+ - lr_scheduler_warmup_steps: 10
121
+ - num_epochs: 4
122
+
123
+ ### Training results
124
+
125
+ | Training Loss | Epoch | Step | Validation Loss |
126
+ |:-------------:|:-----:|:----:|:---------------:|
127
+ | 0.8629 | 0.0 | 1 | 0.9370 |
128
+ | 0.6917 | 0.25 | 119 | 0.8805 |
129
+ | 0.9783 | 0.5 | 238 | 0.8783 |
130
+ | 0.9578 | 0.75 | 357 | 0.8827 |
131
+ | 0.4772 | 1.0 | 476 | 0.8900 |
132
+ | 0.4653 | 1.25 | 595 | 0.9620 |
133
+ | 0.5907 | 1.5 | 714 | 0.9532 |
134
+ | 0.7364 | 1.75 | 833 | 0.9360 |
135
+ | 0.2611 | 2.0 | 952 | 0.9570 |
136
+ | 0.1999 | 2.25 | 1071 | 1.0415 |
137
+ | 0.1532 | 2.51 | 1190 | 1.0776 |
138
+ | 0.0455 | 2.76 | 1309 | 1.0920 |
139
+ | 0.087 | 3.01 | 1428 | 1.1094 |
140
+ | 0.0183 | 3.26 | 1547 | 1.2266 |
141
+ | 0.0135 | 3.51 | 1666 | 1.2604 |
142
+ | 0.1929 | 3.76 | 1785 | 1.2553 |
143
+
144
+
145
+ ### Framework versions
146
+
147
+ - PEFT 0.10.0
148
+ - Transformers 4.40.0.dev0
149
+ - Pytorch 2.1.2+cu118
150
+ - Datasets 2.18.0
151
+ - Tokenizers 0.15.0
adapter_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "Qwen/Qwen1.5-MoE-A2.7B",
5
+ "bias": "none",
6
+ "fan_in_fan_out": null,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 16,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 32,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "gate",
24
+ "k_proj",
25
+ "up_proj",
26
+ "v_proj",
27
+ "o_proj",
28
+ "gate_proj",
29
+ "shared_expert_gate",
30
+ "q_proj",
31
+ "down_proj"
32
+ ],
33
+ "task_type": "CAUSAL_LM",
34
+ "use_dora": false,
35
+ "use_rslora": false
36
+ }
adapter_model.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:b4e646acfb9fe47cc84526e72d8ebad6e93d56a2fd915b9f046a2b64decdc067
3
+ size 2048275154
added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<|endoftext|>": 151643,
3
+ "<|im_end|>": 151645,
4
+ "<|im_start|>": 151644
5
+ }
checkpoint-1425/README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.10.0
checkpoint-1425/adapter_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "Qwen/Qwen1.5-MoE-A2.7B",
5
+ "bias": "none",
6
+ "fan_in_fan_out": null,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 16,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 32,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "gate",
24
+ "k_proj",
25
+ "up_proj",
26
+ "v_proj",
27
+ "o_proj",
28
+ "gate_proj",
29
+ "shared_expert_gate",
30
+ "q_proj",
31
+ "down_proj"
32
+ ],
33
+ "task_type": "CAUSAL_LM",
34
+ "use_dora": false,
35
+ "use_rslora": false
36
+ }
checkpoint-1425/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:39397b262953cee50ba6700a289ec36963b2dbf8f14fd531aa59b7f2a75d7e18
3
+ size 2046221176
checkpoint-1425/added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<|endoftext|>": 151643,
3
+ "<|im_end|>": 151645,
4
+ "<|im_start|>": 151644
5
+ }
checkpoint-1425/merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1425/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1d4de42a4cdcf27f5dbd07f1afcd899a98646a7dd44c7d4cf9305d7da2d36a87
3
+ size 1035165446
checkpoint-1425/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0151af1c8026eeaf57a9210a2159908878492943f90054e4ec8a87ca920f5a15
3
+ size 14244
checkpoint-1425/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8e6ba97f0db9a24831fceacc8f55345fec933ba2886e40df551479fd07daac7a
3
+ size 1064
checkpoint-1425/special_tokens_map.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "eos_token": {
7
+ "content": "<|endoftext|>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false
12
+ },
13
+ "pad_token": {
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ }
20
+ }
checkpoint-1425/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1425/tokenizer_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "added_tokens_decoder": {
4
+ "151643": {
5
+ "content": "<|endoftext|>",
6
+ "lstrip": false,
7
+ "normalized": false,
8
+ "rstrip": false,
9
+ "single_word": false,
10
+ "special": true
11
+ },
12
+ "151644": {
13
+ "content": "<|im_start|>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false,
18
+ "special": true
19
+ },
20
+ "151645": {
21
+ "content": "<|im_end|>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false,
26
+ "special": true
27
+ }
28
+ },
29
+ "additional_special_tokens": [
30
+ "<|im_start|>",
31
+ "<|im_end|>"
32
+ ],
33
+ "bos_token": null,
34
+ "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
35
+ "clean_up_tokenization_spaces": false,
36
+ "eos_token": "<|endoftext|>",
37
+ "errors": "replace",
38
+ "model_max_length": 32768,
39
+ "pad_token": "<|endoftext|>",
40
+ "split_special_tokens": false,
41
+ "tokenizer_class": "Qwen2Tokenizer",
42
+ "unk_token": null
43
+ }
checkpoint-1425/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1425/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a86f7a599d796e7b48de991f534e6b4a66f2f12ac3aac1d99bcffb3fefbdb76d
3
+ size 5816
checkpoint-1425/vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1900/README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.10.0
checkpoint-1900/adapter_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "Qwen/Qwen1.5-MoE-A2.7B",
5
+ "bias": "none",
6
+ "fan_in_fan_out": null,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 16,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 32,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "gate",
24
+ "k_proj",
25
+ "up_proj",
26
+ "v_proj",
27
+ "o_proj",
28
+ "gate_proj",
29
+ "shared_expert_gate",
30
+ "q_proj",
31
+ "down_proj"
32
+ ],
33
+ "task_type": "CAUSAL_LM",
34
+ "use_dora": false,
35
+ "use_rslora": false
36
+ }
checkpoint-1900/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4699694b8bcbffbaaf3317fb5e33bb899438d759504571c2b778241823bfc64d
3
+ size 2046221176
checkpoint-1900/added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<|endoftext|>": 151643,
3
+ "<|im_end|>": 151645,
4
+ "<|im_start|>": 151644
5
+ }
checkpoint-1900/merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1900/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d722de2017d50000f2c35fa44be3c6d1084dd81ee4c129572520a1822ed98bcd
3
+ size 1035165446
checkpoint-1900/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:39cd718f410a0001352be67900cc95f8033ab65a1e56f98e06ce65e07f9a9ee4
3
+ size 14244
checkpoint-1900/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:2ad8e63835341e965b25000c1b2abf3138a503da06dff17129ac435726dddc32
3
+ size 1064
checkpoint-1900/special_tokens_map.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "eos_token": {
7
+ "content": "<|endoftext|>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false
12
+ },
13
+ "pad_token": {
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ }
20
+ }
checkpoint-1900/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1900/tokenizer_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "added_tokens_decoder": {
4
+ "151643": {
5
+ "content": "<|endoftext|>",
6
+ "lstrip": false,
7
+ "normalized": false,
8
+ "rstrip": false,
9
+ "single_word": false,
10
+ "special": true
11
+ },
12
+ "151644": {
13
+ "content": "<|im_start|>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false,
18
+ "special": true
19
+ },
20
+ "151645": {
21
+ "content": "<|im_end|>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false,
26
+ "special": true
27
+ }
28
+ },
29
+ "additional_special_tokens": [
30
+ "<|im_start|>",
31
+ "<|im_end|>"
32
+ ],
33
+ "bos_token": null,
34
+ "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
35
+ "clean_up_tokenization_spaces": false,
36
+ "eos_token": "<|endoftext|>",
37
+ "errors": "replace",
38
+ "model_max_length": 32768,
39
+ "pad_token": "<|endoftext|>",
40
+ "split_special_tokens": false,
41
+ "tokenizer_class": "Qwen2Tokenizer",
42
+ "unk_token": null
43
+ }
checkpoint-1900/trainer_state.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-1900/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a86f7a599d796e7b48de991f534e6b4a66f2f12ac3aac1d99bcffb3fefbdb76d
3
+ size 5816
checkpoint-1900/vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-475/README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.10.0
checkpoint-475/adapter_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "Qwen/Qwen1.5-MoE-A2.7B",
5
+ "bias": "none",
6
+ "fan_in_fan_out": null,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 16,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 32,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "gate",
24
+ "k_proj",
25
+ "up_proj",
26
+ "v_proj",
27
+ "o_proj",
28
+ "gate_proj",
29
+ "shared_expert_gate",
30
+ "q_proj",
31
+ "down_proj"
32
+ ],
33
+ "task_type": "CAUSAL_LM",
34
+ "use_dora": false,
35
+ "use_rslora": false
36
+ }
checkpoint-475/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4627e913181f4c277829ffa2bf25f00aa67b9ca66e1d8e289c4104823fbe8a9e
3
+ size 2046221176
checkpoint-475/added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<|endoftext|>": 151643,
3
+ "<|im_end|>": 151645,
4
+ "<|im_start|>": 151644
5
+ }
checkpoint-475/merges.txt ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-475/optimizer.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3409eea3a658828d7c53c1c009dff40a8d879cd2cbeb27a172a88c3dd178dbc9
3
+ size 1035165446
checkpoint-475/rng_state.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:785d92de068d83d0334d184d93e86bdaaccda6531e4fd20d4f5fc74046802330
3
+ size 14244
checkpoint-475/scheduler.pt ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f0fcd3293e4a09b8bf1933acfa5b9fdbd1d070eaf82e1bcd8a23bec4f4aab5fd
3
+ size 1064
checkpoint-475/special_tokens_map.json ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "additional_special_tokens": [
3
+ "<|im_start|>",
4
+ "<|im_end|>"
5
+ ],
6
+ "eos_token": {
7
+ "content": "<|endoftext|>",
8
+ "lstrip": false,
9
+ "normalized": false,
10
+ "rstrip": false,
11
+ "single_word": false
12
+ },
13
+ "pad_token": {
14
+ "content": "<|endoftext|>",
15
+ "lstrip": false,
16
+ "normalized": false,
17
+ "rstrip": false,
18
+ "single_word": false
19
+ }
20
+ }
checkpoint-475/tokenizer.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-475/tokenizer_config.json ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "add_prefix_space": false,
3
+ "added_tokens_decoder": {
4
+ "151643": {
5
+ "content": "<|endoftext|>",
6
+ "lstrip": false,
7
+ "normalized": false,
8
+ "rstrip": false,
9
+ "single_word": false,
10
+ "special": true
11
+ },
12
+ "151644": {
13
+ "content": "<|im_start|>",
14
+ "lstrip": false,
15
+ "normalized": false,
16
+ "rstrip": false,
17
+ "single_word": false,
18
+ "special": true
19
+ },
20
+ "151645": {
21
+ "content": "<|im_end|>",
22
+ "lstrip": false,
23
+ "normalized": false,
24
+ "rstrip": false,
25
+ "single_word": false,
26
+ "special": true
27
+ }
28
+ },
29
+ "additional_special_tokens": [
30
+ "<|im_start|>",
31
+ "<|im_end|>"
32
+ ],
33
+ "bos_token": null,
34
+ "chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
35
+ "clean_up_tokenization_spaces": false,
36
+ "eos_token": "<|endoftext|>",
37
+ "errors": "replace",
38
+ "model_max_length": 32768,
39
+ "pad_token": "<|endoftext|>",
40
+ "split_special_tokens": false,
41
+ "tokenizer_class": "Qwen2Tokenizer",
42
+ "unk_token": null
43
+ }
checkpoint-475/trainer_state.json ADDED
@@ -0,0 +1,3378 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "best_metric": null,
3
+ "best_model_checkpoint": null,
4
+ "epoch": 1.0,
5
+ "eval_steps": 119,
6
+ "global_step": 475,
7
+ "is_hyper_param_search": false,
8
+ "is_local_process_zero": true,
9
+ "is_world_process_zero": true,
10
+ "log_history": [
11
+ {
12
+ "epoch": 0.0,
13
+ "grad_norm": 0.434104859828949,
14
+ "learning_rate": 2e-05,
15
+ "loss": 0.8629,
16
+ "step": 1
17
+ },
18
+ {
19
+ "epoch": 0.0,
20
+ "eval_loss": 0.9369699358940125,
21
+ "eval_runtime": 61.2717,
22
+ "eval_samples_per_second": 1.632,
23
+ "eval_steps_per_second": 1.632,
24
+ "step": 1
25
+ },
26
+ {
27
+ "epoch": 0.0,
28
+ "grad_norm": 0.517804741859436,
29
+ "learning_rate": 4e-05,
30
+ "loss": 0.9794,
31
+ "step": 2
32
+ },
33
+ {
34
+ "epoch": 0.01,
35
+ "grad_norm": 1.3174158334732056,
36
+ "learning_rate": 6e-05,
37
+ "loss": 1.5473,
38
+ "step": 3
39
+ },
40
+ {
41
+ "epoch": 0.01,
42
+ "grad_norm": 0.6236504912376404,
43
+ "learning_rate": 8e-05,
44
+ "loss": 0.6592,
45
+ "step": 4
46
+ },
47
+ {
48
+ "epoch": 0.01,
49
+ "grad_norm": 0.49080607295036316,
50
+ "learning_rate": 0.0001,
51
+ "loss": 1.1104,
52
+ "step": 5
53
+ },
54
+ {
55
+ "epoch": 0.01,
56
+ "grad_norm": 0.43938764929771423,
57
+ "learning_rate": 0.00012,
58
+ "loss": 0.9446,
59
+ "step": 6
60
+ },
61
+ {
62
+ "epoch": 0.01,
63
+ "grad_norm": 0.8905380368232727,
64
+ "learning_rate": 0.00014,
65
+ "loss": 0.6719,
66
+ "step": 7
67
+ },
68
+ {
69
+ "epoch": 0.02,
70
+ "grad_norm": 0.9044575095176697,
71
+ "learning_rate": 0.00016,
72
+ "loss": 0.6471,
73
+ "step": 8
74
+ },
75
+ {
76
+ "epoch": 0.02,
77
+ "grad_norm": 0.541214108467102,
78
+ "learning_rate": 0.00018,
79
+ "loss": 0.8339,
80
+ "step": 9
81
+ },
82
+ {
83
+ "epoch": 0.02,
84
+ "grad_norm": 1.075484275817871,
85
+ "learning_rate": 0.0002,
86
+ "loss": 0.9616,
87
+ "step": 10
88
+ },
89
+ {
90
+ "epoch": 0.02,
91
+ "grad_norm": 0.3413633406162262,
92
+ "learning_rate": 0.0001999998618515421,
93
+ "loss": 0.8251,
94
+ "step": 11
95
+ },
96
+ {
97
+ "epoch": 0.03,
98
+ "grad_norm": 0.7642049193382263,
99
+ "learning_rate": 0.00019999944740655014,
100
+ "loss": 1.3883,
101
+ "step": 12
102
+ },
103
+ {
104
+ "epoch": 0.03,
105
+ "grad_norm": 0.5759286284446716,
106
+ "learning_rate": 0.00019999875666616918,
107
+ "loss": 0.9162,
108
+ "step": 13
109
+ },
110
+ {
111
+ "epoch": 0.03,
112
+ "grad_norm": 1.16646409034729,
113
+ "learning_rate": 0.00019999778963230775,
114
+ "loss": 1.1735,
115
+ "step": 14
116
+ },
117
+ {
118
+ "epoch": 0.03,
119
+ "grad_norm": 0.5447037220001221,
120
+ "learning_rate": 0.0001999965463076377,
121
+ "loss": 1.2769,
122
+ "step": 15
123
+ },
124
+ {
125
+ "epoch": 0.03,
126
+ "grad_norm": 0.510498046875,
127
+ "learning_rate": 0.00019999502669559432,
128
+ "loss": 1.1439,
129
+ "step": 16
130
+ },
131
+ {
132
+ "epoch": 0.04,
133
+ "grad_norm": 0.514502227306366,
134
+ "learning_rate": 0.00019999323080037624,
135
+ "loss": 0.9929,
136
+ "step": 17
137
+ },
138
+ {
139
+ "epoch": 0.04,
140
+ "grad_norm": 0.2014903873205185,
141
+ "learning_rate": 0.00019999115862694546,
142
+ "loss": 0.6706,
143
+ "step": 18
144
+ },
145
+ {
146
+ "epoch": 0.04,
147
+ "grad_norm": 0.6941856741905212,
148
+ "learning_rate": 0.00019998881018102737,
149
+ "loss": 0.7576,
150
+ "step": 19
151
+ },
152
+ {
153
+ "epoch": 0.04,
154
+ "grad_norm": 0.29424500465393066,
155
+ "learning_rate": 0.00019998618546911056,
156
+ "loss": 1.5048,
157
+ "step": 20
158
+ },
159
+ {
160
+ "epoch": 0.04,
161
+ "grad_norm": 1.431168556213379,
162
+ "learning_rate": 0.00019998328449844714,
163
+ "loss": 0.761,
164
+ "step": 21
165
+ },
166
+ {
167
+ "epoch": 0.05,
168
+ "grad_norm": 2.672117233276367,
169
+ "learning_rate": 0.00019998010727705236,
170
+ "loss": 0.9434,
171
+ "step": 22
172
+ },
173
+ {
174
+ "epoch": 0.05,
175
+ "grad_norm": 0.40351876616477966,
176
+ "learning_rate": 0.00019997665381370477,
177
+ "loss": 1.2708,
178
+ "step": 23
179
+ },
180
+ {
181
+ "epoch": 0.05,
182
+ "grad_norm": 0.36313024163246155,
183
+ "learning_rate": 0.00019997292411794618,
184
+ "loss": 0.6931,
185
+ "step": 24
186
+ },
187
+ {
188
+ "epoch": 0.05,
189
+ "grad_norm": 0.7412884831428528,
190
+ "learning_rate": 0.00019996891820008164,
191
+ "loss": 0.827,
192
+ "step": 25
193
+ },
194
+ {
195
+ "epoch": 0.05,
196
+ "grad_norm": 0.7299063205718994,
197
+ "learning_rate": 0.00019996463607117935,
198
+ "loss": 0.7135,
199
+ "step": 26
200
+ },
201
+ {
202
+ "epoch": 0.06,
203
+ "grad_norm": 0.5188469290733337,
204
+ "learning_rate": 0.00019996007774307075,
205
+ "loss": 0.574,
206
+ "step": 27
207
+ },
208
+ {
209
+ "epoch": 0.06,
210
+ "grad_norm": 0.23084047436714172,
211
+ "learning_rate": 0.00019995524322835034,
212
+ "loss": 1.0736,
213
+ "step": 28
214
+ },
215
+ {
216
+ "epoch": 0.06,
217
+ "grad_norm": 0.7209138870239258,
218
+ "learning_rate": 0.00019995013254037574,
219
+ "loss": 0.9087,
220
+ "step": 29
221
+ },
222
+ {
223
+ "epoch": 0.06,
224
+ "grad_norm": 1.2266876697540283,
225
+ "learning_rate": 0.00019994474569326757,
226
+ "loss": 1.2464,
227
+ "step": 30
228
+ },
229
+ {
230
+ "epoch": 0.07,
231
+ "grad_norm": 0.6613232493400574,
232
+ "learning_rate": 0.0001999390827019096,
233
+ "loss": 0.9568,
234
+ "step": 31
235
+ },
236
+ {
237
+ "epoch": 0.07,
238
+ "grad_norm": 1.0048911571502686,
239
+ "learning_rate": 0.00019993314358194843,
240
+ "loss": 1.1935,
241
+ "step": 32
242
+ },
243
+ {
244
+ "epoch": 0.07,
245
+ "grad_norm": 0.9797032475471497,
246
+ "learning_rate": 0.00019992692834979372,
247
+ "loss": 1.1969,
248
+ "step": 33
249
+ },
250
+ {
251
+ "epoch": 0.07,
252
+ "grad_norm": 0.4784688353538513,
253
+ "learning_rate": 0.00019992043702261793,
254
+ "loss": 0.8674,
255
+ "step": 34
256
+ },
257
+ {
258
+ "epoch": 0.07,
259
+ "grad_norm": 2.863600015640259,
260
+ "learning_rate": 0.00019991366961835642,
261
+ "loss": 1.3276,
262
+ "step": 35
263
+ },
264
+ {
265
+ "epoch": 0.08,
266
+ "grad_norm": 0.4809297025203705,
267
+ "learning_rate": 0.0001999066261557073,
268
+ "loss": 1.0116,
269
+ "step": 36
270
+ },
271
+ {
272
+ "epoch": 0.08,
273
+ "grad_norm": 0.6229730844497681,
274
+ "learning_rate": 0.00019989930665413147,
275
+ "loss": 1.2794,
276
+ "step": 37
277
+ },
278
+ {
279
+ "epoch": 0.08,
280
+ "grad_norm": 0.5807106494903564,
281
+ "learning_rate": 0.0001998917111338525,
282
+ "loss": 1.0074,
283
+ "step": 38
284
+ },
285
+ {
286
+ "epoch": 0.08,
287
+ "grad_norm": 1.5811257362365723,
288
+ "learning_rate": 0.00019988383961585645,
289
+ "loss": 1.2491,
290
+ "step": 39
291
+ },
292
+ {
293
+ "epoch": 0.08,
294
+ "grad_norm": 0.46693727374076843,
295
+ "learning_rate": 0.00019987569212189224,
296
+ "loss": 0.8432,
297
+ "step": 40
298
+ },
299
+ {
300
+ "epoch": 0.09,
301
+ "grad_norm": 0.9792713522911072,
302
+ "learning_rate": 0.00019986726867447107,
303
+ "loss": 0.8716,
304
+ "step": 41
305
+ },
306
+ {
307
+ "epoch": 0.09,
308
+ "grad_norm": 0.3240562081336975,
309
+ "learning_rate": 0.00019985856929686667,
310
+ "loss": 0.9934,
311
+ "step": 42
312
+ },
313
+ {
314
+ "epoch": 0.09,
315
+ "grad_norm": 0.7358172535896301,
316
+ "learning_rate": 0.0001998495940131152,
317
+ "loss": 0.7247,
318
+ "step": 43
319
+ },
320
+ {
321
+ "epoch": 0.09,
322
+ "grad_norm": 0.40497535467147827,
323
+ "learning_rate": 0.00019984034284801502,
324
+ "loss": 0.8262,
325
+ "step": 44
326
+ },
327
+ {
328
+ "epoch": 0.09,
329
+ "grad_norm": 0.36835265159606934,
330
+ "learning_rate": 0.00019983081582712685,
331
+ "loss": 1.0898,
332
+ "step": 45
333
+ },
334
+ {
335
+ "epoch": 0.1,
336
+ "grad_norm": 0.7027626633644104,
337
+ "learning_rate": 0.0001998210129767735,
338
+ "loss": 1.2949,
339
+ "step": 46
340
+ },
341
+ {
342
+ "epoch": 0.1,
343
+ "grad_norm": 0.35624387860298157,
344
+ "learning_rate": 0.00019981093432404006,
345
+ "loss": 0.9734,
346
+ "step": 47
347
+ },
348
+ {
349
+ "epoch": 0.1,
350
+ "grad_norm": 0.4896808862686157,
351
+ "learning_rate": 0.00019980057989677345,
352
+ "loss": 1.2297,
353
+ "step": 48
354
+ },
355
+ {
356
+ "epoch": 0.1,
357
+ "grad_norm": 0.9656649827957153,
358
+ "learning_rate": 0.00019978994972358265,
359
+ "loss": 0.8358,
360
+ "step": 49
361
+ },
362
+ {
363
+ "epoch": 0.11,
364
+ "grad_norm": 0.644644021987915,
365
+ "learning_rate": 0.0001997790438338385,
366
+ "loss": 0.7373,
367
+ "step": 50
368
+ },
369
+ {
370
+ "epoch": 0.11,
371
+ "grad_norm": 1.0634011030197144,
372
+ "learning_rate": 0.00019976786225767365,
373
+ "loss": 1.3792,
374
+ "step": 51
375
+ },
376
+ {
377
+ "epoch": 0.11,
378
+ "grad_norm": 0.9041422605514526,
379
+ "learning_rate": 0.00019975640502598244,
380
+ "loss": 0.856,
381
+ "step": 52
382
+ },
383
+ {
384
+ "epoch": 0.11,
385
+ "grad_norm": 0.5682844519615173,
386
+ "learning_rate": 0.00019974467217042085,
387
+ "loss": 0.6686,
388
+ "step": 53
389
+ },
390
+ {
391
+ "epoch": 0.11,
392
+ "grad_norm": 0.7197520136833191,
393
+ "learning_rate": 0.00019973266372340639,
394
+ "loss": 0.6806,
395
+ "step": 54
396
+ },
397
+ {
398
+ "epoch": 0.12,
399
+ "grad_norm": 1.2542258501052856,
400
+ "learning_rate": 0.00019972037971811802,
401
+ "loss": 1.353,
402
+ "step": 55
403
+ },
404
+ {
405
+ "epoch": 0.12,
406
+ "grad_norm": 0.6802650094032288,
407
+ "learning_rate": 0.0001997078201884961,
408
+ "loss": 1.0014,
409
+ "step": 56
410
+ },
411
+ {
412
+ "epoch": 0.12,
413
+ "grad_norm": 0.4886693060398102,
414
+ "learning_rate": 0.0001996949851692422,
415
+ "loss": 0.8613,
416
+ "step": 57
417
+ },
418
+ {
419
+ "epoch": 0.12,
420
+ "grad_norm": 1.0226856470108032,
421
+ "learning_rate": 0.0001996818746958191,
422
+ "loss": 1.0443,
423
+ "step": 58
424
+ },
425
+ {
426
+ "epoch": 0.12,
427
+ "grad_norm": 0.5514834523200989,
428
+ "learning_rate": 0.00019966848880445062,
429
+ "loss": 0.8816,
430
+ "step": 59
431
+ },
432
+ {
433
+ "epoch": 0.13,
434
+ "grad_norm": 0.4890052378177643,
435
+ "learning_rate": 0.00019965482753212156,
436
+ "loss": 0.8541,
437
+ "step": 60
438
+ },
439
+ {
440
+ "epoch": 0.13,
441
+ "grad_norm": 0.9011398553848267,
442
+ "learning_rate": 0.0001996408909165776,
443
+ "loss": 1.1158,
444
+ "step": 61
445
+ },
446
+ {
447
+ "epoch": 0.13,
448
+ "grad_norm": 0.7809276580810547,
449
+ "learning_rate": 0.00019962667899632518,
450
+ "loss": 0.6702,
451
+ "step": 62
452
+ },
453
+ {
454
+ "epoch": 0.13,
455
+ "grad_norm": 0.604097843170166,
456
+ "learning_rate": 0.00019961219181063142,
457
+ "loss": 1.1875,
458
+ "step": 63
459
+ },
460
+ {
461
+ "epoch": 0.13,
462
+ "grad_norm": 0.9003333449363708,
463
+ "learning_rate": 0.00019959742939952392,
464
+ "loss": 1.2126,
465
+ "step": 64
466
+ },
467
+ {
468
+ "epoch": 0.14,
469
+ "grad_norm": 0.59239661693573,
470
+ "learning_rate": 0.0001995823918037908,
471
+ "loss": 1.1083,
472
+ "step": 65
473
+ },
474
+ {
475
+ "epoch": 0.14,
476
+ "grad_norm": 0.6981655955314636,
477
+ "learning_rate": 0.00019956707906498044,
478
+ "loss": 0.9634,
479
+ "step": 66
480
+ },
481
+ {
482
+ "epoch": 0.14,
483
+ "grad_norm": 0.4635857045650482,
484
+ "learning_rate": 0.00019955149122540152,
485
+ "loss": 1.0345,
486
+ "step": 67
487
+ },
488
+ {
489
+ "epoch": 0.14,
490
+ "grad_norm": 0.35098958015441895,
491
+ "learning_rate": 0.00019953562832812272,
492
+ "loss": 0.8871,
493
+ "step": 68
494
+ },
495
+ {
496
+ "epoch": 0.15,
497
+ "grad_norm": 0.4510989189147949,
498
+ "learning_rate": 0.00019951949041697274,
499
+ "loss": 0.6967,
500
+ "step": 69
501
+ },
502
+ {
503
+ "epoch": 0.15,
504
+ "grad_norm": 0.39661434292793274,
505
+ "learning_rate": 0.00019950307753654017,
506
+ "loss": 1.0786,
507
+ "step": 70
508
+ },
509
+ {
510
+ "epoch": 0.15,
511
+ "grad_norm": 0.5836047530174255,
512
+ "learning_rate": 0.00019948638973217323,
513
+ "loss": 0.9265,
514
+ "step": 71
515
+ },
516
+ {
517
+ "epoch": 0.15,
518
+ "grad_norm": 0.4589115381240845,
519
+ "learning_rate": 0.00019946942704997982,
520
+ "loss": 0.6476,
521
+ "step": 72
522
+ },
523
+ {
524
+ "epoch": 0.15,
525
+ "grad_norm": 0.4495834410190582,
526
+ "learning_rate": 0.00019945218953682734,
527
+ "loss": 0.8693,
528
+ "step": 73
529
+ },
530
+ {
531
+ "epoch": 0.16,
532
+ "grad_norm": 0.35905730724334717,
533
+ "learning_rate": 0.00019943467724034252,
534
+ "loss": 1.0325,
535
+ "step": 74
536
+ },
537
+ {
538
+ "epoch": 0.16,
539
+ "grad_norm": 2.235016345977783,
540
+ "learning_rate": 0.0001994168902089112,
541
+ "loss": 1.3103,
542
+ "step": 75
543
+ },
544
+ {
545
+ "epoch": 0.16,
546
+ "grad_norm": 0.36725524067878723,
547
+ "learning_rate": 0.00019939882849167852,
548
+ "loss": 0.908,
549
+ "step": 76
550
+ },
551
+ {
552
+ "epoch": 0.16,
553
+ "grad_norm": 0.66635662317276,
554
+ "learning_rate": 0.0001993804921385484,
555
+ "loss": 0.682,
556
+ "step": 77
557
+ },
558
+ {
559
+ "epoch": 0.16,
560
+ "grad_norm": 2.121004819869995,
561
+ "learning_rate": 0.0001993618812001836,
562
+ "loss": 0.8462,
563
+ "step": 78
564
+ },
565
+ {
566
+ "epoch": 0.17,
567
+ "grad_norm": 0.44895172119140625,
568
+ "learning_rate": 0.00019934299572800556,
569
+ "loss": 0.9625,
570
+ "step": 79
571
+ },
572
+ {
573
+ "epoch": 0.17,
574
+ "grad_norm": 0.5769445300102234,
575
+ "learning_rate": 0.00019932383577419432,
576
+ "loss": 0.7278,
577
+ "step": 80
578
+ },
579
+ {
580
+ "epoch": 0.17,
581
+ "grad_norm": 0.3807710111141205,
582
+ "learning_rate": 0.00019930440139168817,
583
+ "loss": 0.657,
584
+ "step": 81
585
+ },
586
+ {
587
+ "epoch": 0.17,
588
+ "grad_norm": 0.212838813662529,
589
+ "learning_rate": 0.00019928469263418374,
590
+ "loss": 0.3094,
591
+ "step": 82
592
+ },
593
+ {
594
+ "epoch": 0.17,
595
+ "grad_norm": 0.8039274215698242,
596
+ "learning_rate": 0.0001992647095561357,
597
+ "loss": 0.8898,
598
+ "step": 83
599
+ },
600
+ {
601
+ "epoch": 0.18,
602
+ "grad_norm": 0.7184070348739624,
603
+ "learning_rate": 0.00019924445221275675,
604
+ "loss": 0.8613,
605
+ "step": 84
606
+ },
607
+ {
608
+ "epoch": 0.18,
609
+ "grad_norm": 0.4697589874267578,
610
+ "learning_rate": 0.00019922392066001722,
611
+ "loss": 0.9533,
612
+ "step": 85
613
+ },
614
+ {
615
+ "epoch": 0.18,
616
+ "grad_norm": 0.7024903297424316,
617
+ "learning_rate": 0.00019920311495464518,
618
+ "loss": 0.7188,
619
+ "step": 86
620
+ },
621
+ {
622
+ "epoch": 0.18,
623
+ "grad_norm": 0.5008754730224609,
624
+ "learning_rate": 0.00019918203515412617,
625
+ "loss": 0.8329,
626
+ "step": 87
627
+ },
628
+ {
629
+ "epoch": 0.19,
630
+ "grad_norm": 0.6406499743461609,
631
+ "learning_rate": 0.00019916068131670302,
632
+ "loss": 1.4259,
633
+ "step": 88
634
+ },
635
+ {
636
+ "epoch": 0.19,
637
+ "grad_norm": 0.39489614963531494,
638
+ "learning_rate": 0.00019913905350137573,
639
+ "loss": 0.5831,
640
+ "step": 89
641
+ },
642
+ {
643
+ "epoch": 0.19,
644
+ "grad_norm": 0.7018424272537231,
645
+ "learning_rate": 0.0001991171517679013,
646
+ "loss": 0.9808,
647
+ "step": 90
648
+ },
649
+ {
650
+ "epoch": 0.19,
651
+ "grad_norm": 0.5011338591575623,
652
+ "learning_rate": 0.00019909497617679348,
653
+ "loss": 0.6806,
654
+ "step": 91
655
+ },
656
+ {
657
+ "epoch": 0.19,
658
+ "grad_norm": 0.2412053793668747,
659
+ "learning_rate": 0.0001990725267893228,
660
+ "loss": 1.0299,
661
+ "step": 92
662
+ },
663
+ {
664
+ "epoch": 0.2,
665
+ "grad_norm": 0.4145759046077728,
666
+ "learning_rate": 0.00019904980366751624,
667
+ "loss": 1.4344,
668
+ "step": 93
669
+ },
670
+ {
671
+ "epoch": 0.2,
672
+ "grad_norm": 0.5789082050323486,
673
+ "learning_rate": 0.00019902680687415705,
674
+ "loss": 0.4662,
675
+ "step": 94
676
+ },
677
+ {
678
+ "epoch": 0.2,
679
+ "grad_norm": 0.28986847400665283,
680
+ "learning_rate": 0.00019900353647278466,
681
+ "loss": 1.296,
682
+ "step": 95
683
+ },
684
+ {
685
+ "epoch": 0.2,
686
+ "grad_norm": 0.33722007274627686,
687
+ "learning_rate": 0.00019897999252769448,
688
+ "loss": 0.8011,
689
+ "step": 96
690
+ },
691
+ {
692
+ "epoch": 0.2,
693
+ "grad_norm": 0.6796722412109375,
694
+ "learning_rate": 0.00019895617510393772,
695
+ "loss": 0.972,
696
+ "step": 97
697
+ },
698
+ {
699
+ "epoch": 0.21,
700
+ "grad_norm": 0.5858548879623413,
701
+ "learning_rate": 0.00019893208426732115,
702
+ "loss": 1.0073,
703
+ "step": 98
704
+ },
705
+ {
706
+ "epoch": 0.21,
707
+ "grad_norm": 0.5766484141349792,
708
+ "learning_rate": 0.00019890772008440704,
709
+ "loss": 0.7884,
710
+ "step": 99
711
+ },
712
+ {
713
+ "epoch": 0.21,
714
+ "grad_norm": 0.37647876143455505,
715
+ "learning_rate": 0.00019888308262251285,
716
+ "loss": 0.6407,
717
+ "step": 100
718
+ },
719
+ {
720
+ "epoch": 0.21,
721
+ "grad_norm": 1.5475112199783325,
722
+ "learning_rate": 0.00019885817194971117,
723
+ "loss": 1.1401,
724
+ "step": 101
725
+ },
726
+ {
727
+ "epoch": 0.21,
728
+ "grad_norm": 1.1082801818847656,
729
+ "learning_rate": 0.00019883298813482938,
730
+ "loss": 1.392,
731
+ "step": 102
732
+ },
733
+ {
734
+ "epoch": 0.22,
735
+ "grad_norm": 0.3952051103115082,
736
+ "learning_rate": 0.00019880753124744963,
737
+ "loss": 1.0498,
738
+ "step": 103
739
+ },
740
+ {
741
+ "epoch": 0.22,
742
+ "grad_norm": 0.19289755821228027,
743
+ "learning_rate": 0.00019878180135790845,
744
+ "loss": 0.4145,
745
+ "step": 104
746
+ },
747
+ {
748
+ "epoch": 0.22,
749
+ "grad_norm": 0.5658400654792786,
750
+ "learning_rate": 0.00019875579853729676,
751
+ "loss": 1.0984,
752
+ "step": 105
753
+ },
754
+ {
755
+ "epoch": 0.22,
756
+ "grad_norm": 0.8976437449455261,
757
+ "learning_rate": 0.00019872952285745959,
758
+ "loss": 0.6919,
759
+ "step": 106
760
+ },
761
+ {
762
+ "epoch": 0.23,
763
+ "grad_norm": 0.5265024900436401,
764
+ "learning_rate": 0.00019870297439099577,
765
+ "loss": 1.2932,
766
+ "step": 107
767
+ },
768
+ {
769
+ "epoch": 0.23,
770
+ "grad_norm": 0.8367021083831787,
771
+ "learning_rate": 0.00019867615321125795,
772
+ "loss": 1.4497,
773
+ "step": 108
774
+ },
775
+ {
776
+ "epoch": 0.23,
777
+ "grad_norm": 0.5223955512046814,
778
+ "learning_rate": 0.00019864905939235214,
779
+ "loss": 1.0325,
780
+ "step": 109
781
+ },
782
+ {
783
+ "epoch": 0.23,
784
+ "grad_norm": 1.408779501914978,
785
+ "learning_rate": 0.00019862169300913785,
786
+ "loss": 0.9026,
787
+ "step": 110
788
+ },
789
+ {
790
+ "epoch": 0.23,
791
+ "grad_norm": 0.24817530810832977,
792
+ "learning_rate": 0.00019859405413722746,
793
+ "loss": 0.826,
794
+ "step": 111
795
+ },
796
+ {
797
+ "epoch": 0.24,
798
+ "grad_norm": 1.450430154800415,
799
+ "learning_rate": 0.0001985661428529863,
800
+ "loss": 0.9791,
801
+ "step": 112
802
+ },
803
+ {
804
+ "epoch": 0.24,
805
+ "grad_norm": 0.4882315993309021,
806
+ "learning_rate": 0.0001985379592335325,
807
+ "loss": 0.7889,
808
+ "step": 113
809
+ },
810
+ {
811
+ "epoch": 0.24,
812
+ "grad_norm": 0.42783451080322266,
813
+ "learning_rate": 0.00019850950335673643,
814
+ "loss": 1.1608,
815
+ "step": 114
816
+ },
817
+ {
818
+ "epoch": 0.24,
819
+ "grad_norm": 1.0337790250778198,
820
+ "learning_rate": 0.00019848077530122083,
821
+ "loss": 1.045,
822
+ "step": 115
823
+ },
824
+ {
825
+ "epoch": 0.24,
826
+ "grad_norm": 0.3064819872379303,
827
+ "learning_rate": 0.00019845177514636042,
828
+ "loss": 0.6474,
829
+ "step": 116
830
+ },
831
+ {
832
+ "epoch": 0.25,
833
+ "grad_norm": 0.29662173986434937,
834
+ "learning_rate": 0.00019842250297228176,
835
+ "loss": 0.9493,
836
+ "step": 117
837
+ },
838
+ {
839
+ "epoch": 0.25,
840
+ "grad_norm": 0.515562891960144,
841
+ "learning_rate": 0.00019839295885986296,
842
+ "loss": 1.0605,
843
+ "step": 118
844
+ },
845
+ {
846
+ "epoch": 0.25,
847
+ "grad_norm": 1.4514832496643066,
848
+ "learning_rate": 0.0001983631428907335,
849
+ "loss": 0.6917,
850
+ "step": 119
851
+ },
852
+ {
853
+ "epoch": 0.25,
854
+ "eval_loss": 0.8804921507835388,
855
+ "eval_runtime": 61.5233,
856
+ "eval_samples_per_second": 1.625,
857
+ "eval_steps_per_second": 1.625,
858
+ "step": 119
859
+ },
860
+ {
861
+ "epoch": 0.25,
862
+ "grad_norm": 0.30757004022598267,
863
+ "learning_rate": 0.00019833305514727395,
864
+ "loss": 0.9722,
865
+ "step": 120
866
+ },
867
+ {
868
+ "epoch": 0.25,
869
+ "grad_norm": 0.5162855386734009,
870
+ "learning_rate": 0.00019830269571261583,
871
+ "loss": 1.2197,
872
+ "step": 121
873
+ },
874
+ {
875
+ "epoch": 0.26,
876
+ "grad_norm": 0.5095639824867249,
877
+ "learning_rate": 0.00019827206467064133,
878
+ "loss": 0.8676,
879
+ "step": 122
880
+ },
881
+ {
882
+ "epoch": 0.26,
883
+ "grad_norm": 0.4804045557975769,
884
+ "learning_rate": 0.00019824116210598306,
885
+ "loss": 0.8565,
886
+ "step": 123
887
+ },
888
+ {
889
+ "epoch": 0.26,
890
+ "grad_norm": 0.28008362650871277,
891
+ "learning_rate": 0.0001982099881040239,
892
+ "loss": 0.9001,
893
+ "step": 124
894
+ },
895
+ {
896
+ "epoch": 0.26,
897
+ "grad_norm": 0.6209085583686829,
898
+ "learning_rate": 0.0001981785427508966,
899
+ "loss": 0.7188,
900
+ "step": 125
901
+ },
902
+ {
903
+ "epoch": 0.27,
904
+ "grad_norm": 0.32877278327941895,
905
+ "learning_rate": 0.0001981468261334837,
906
+ "loss": 0.6749,
907
+ "step": 126
908
+ },
909
+ {
910
+ "epoch": 0.27,
911
+ "grad_norm": 0.4256601631641388,
912
+ "learning_rate": 0.00019811483833941728,
913
+ "loss": 0.8086,
914
+ "step": 127
915
+ },
916
+ {
917
+ "epoch": 0.27,
918
+ "grad_norm": 1.1572288274765015,
919
+ "learning_rate": 0.0001980825794570786,
920
+ "loss": 0.8554,
921
+ "step": 128
922
+ },
923
+ {
924
+ "epoch": 0.27,
925
+ "grad_norm": 0.4987819194793701,
926
+ "learning_rate": 0.00019805004957559793,
927
+ "loss": 0.6999,
928
+ "step": 129
929
+ },
930
+ {
931
+ "epoch": 0.27,
932
+ "grad_norm": 0.6852537393569946,
933
+ "learning_rate": 0.00019801724878485438,
934
+ "loss": 0.8759,
935
+ "step": 130
936
+ },
937
+ {
938
+ "epoch": 0.28,
939
+ "grad_norm": 0.7970736622810364,
940
+ "learning_rate": 0.00019798417717547552,
941
+ "loss": 0.7471,
942
+ "step": 131
943
+ },
944
+ {
945
+ "epoch": 0.28,
946
+ "grad_norm": 0.5638220310211182,
947
+ "learning_rate": 0.00019795083483883715,
948
+ "loss": 1.0391,
949
+ "step": 132
950
+ },
951
+ {
952
+ "epoch": 0.28,
953
+ "grad_norm": 0.5482009649276733,
954
+ "learning_rate": 0.00019791722186706317,
955
+ "loss": 0.8363,
956
+ "step": 133
957
+ },
958
+ {
959
+ "epoch": 0.28,
960
+ "grad_norm": 0.23791633546352386,
961
+ "learning_rate": 0.0001978833383530251,
962
+ "loss": 0.725,
963
+ "step": 134
964
+ },
965
+ {
966
+ "epoch": 0.28,
967
+ "grad_norm": 0.5339345335960388,
968
+ "learning_rate": 0.00019784918439034216,
969
+ "loss": 0.9828,
970
+ "step": 135
971
+ },
972
+ {
973
+ "epoch": 0.29,
974
+ "grad_norm": 0.24769064784049988,
975
+ "learning_rate": 0.00019781476007338058,
976
+ "loss": 0.9496,
977
+ "step": 136
978
+ },
979
+ {
980
+ "epoch": 0.29,
981
+ "grad_norm": 0.46634215116500854,
982
+ "learning_rate": 0.00019778006549725375,
983
+ "loss": 1.0973,
984
+ "step": 137
985
+ },
986
+ {
987
+ "epoch": 0.29,
988
+ "grad_norm": 0.8007522821426392,
989
+ "learning_rate": 0.00019774510075782172,
990
+ "loss": 0.6847,
991
+ "step": 138
992
+ },
993
+ {
994
+ "epoch": 0.29,
995
+ "grad_norm": 0.5393804907798767,
996
+ "learning_rate": 0.00019770986595169096,
997
+ "loss": 0.6461,
998
+ "step": 139
999
+ },
1000
+ {
1001
+ "epoch": 0.29,
1002
+ "grad_norm": 0.2891620695590973,
1003
+ "learning_rate": 0.00019767436117621413,
1004
+ "loss": 0.2937,
1005
+ "step": 140
1006
+ },
1007
+ {
1008
+ "epoch": 0.3,
1009
+ "grad_norm": 0.9463545680046082,
1010
+ "learning_rate": 0.0001976385865294899,
1011
+ "loss": 0.4934,
1012
+ "step": 141
1013
+ },
1014
+ {
1015
+ "epoch": 0.3,
1016
+ "grad_norm": 0.25647807121276855,
1017
+ "learning_rate": 0.00019760254211036244,
1018
+ "loss": 0.7446,
1019
+ "step": 142
1020
+ },
1021
+ {
1022
+ "epoch": 0.3,
1023
+ "grad_norm": 0.49435535073280334,
1024
+ "learning_rate": 0.00019756622801842143,
1025
+ "loss": 0.3544,
1026
+ "step": 143
1027
+ },
1028
+ {
1029
+ "epoch": 0.3,
1030
+ "grad_norm": 0.7826042175292969,
1031
+ "learning_rate": 0.00019752964435400155,
1032
+ "loss": 0.6972,
1033
+ "step": 144
1034
+ },
1035
+ {
1036
+ "epoch": 0.31,
1037
+ "grad_norm": 0.7160178422927856,
1038
+ "learning_rate": 0.00019749279121818235,
1039
+ "loss": 0.9655,
1040
+ "step": 145
1041
+ },
1042
+ {
1043
+ "epoch": 0.31,
1044
+ "grad_norm": 0.3925221264362335,
1045
+ "learning_rate": 0.00019745566871278794,
1046
+ "loss": 0.9041,
1047
+ "step": 146
1048
+ },
1049
+ {
1050
+ "epoch": 0.31,
1051
+ "grad_norm": 0.5669321417808533,
1052
+ "learning_rate": 0.0001974182769403866,
1053
+ "loss": 0.9093,
1054
+ "step": 147
1055
+ },
1056
+ {
1057
+ "epoch": 0.31,
1058
+ "grad_norm": 0.5025343298912048,
1059
+ "learning_rate": 0.00019738061600429064,
1060
+ "loss": 0.6226,
1061
+ "step": 148
1062
+ },
1063
+ {
1064
+ "epoch": 0.31,
1065
+ "grad_norm": 1.1127972602844238,
1066
+ "learning_rate": 0.0001973426860085561,
1067
+ "loss": 0.7431,
1068
+ "step": 149
1069
+ },
1070
+ {
1071
+ "epoch": 0.32,
1072
+ "grad_norm": 0.4064362049102783,
1073
+ "learning_rate": 0.00019730448705798239,
1074
+ "loss": 0.8444,
1075
+ "step": 150
1076
+ },
1077
+ {
1078
+ "epoch": 0.32,
1079
+ "grad_norm": 0.9272475242614746,
1080
+ "learning_rate": 0.00019726601925811204,
1081
+ "loss": 0.836,
1082
+ "step": 151
1083
+ },
1084
+ {
1085
+ "epoch": 0.32,
1086
+ "grad_norm": 0.6594715118408203,
1087
+ "learning_rate": 0.00019722728271523034,
1088
+ "loss": 0.9031,
1089
+ "step": 152
1090
+ },
1091
+ {
1092
+ "epoch": 0.32,
1093
+ "grad_norm": 0.9399688839912415,
1094
+ "learning_rate": 0.00019718827753636522,
1095
+ "loss": 0.7959,
1096
+ "step": 153
1097
+ },
1098
+ {
1099
+ "epoch": 0.32,
1100
+ "grad_norm": 0.4452652633190155,
1101
+ "learning_rate": 0.00019714900382928675,
1102
+ "loss": 0.5638,
1103
+ "step": 154
1104
+ },
1105
+ {
1106
+ "epoch": 0.33,
1107
+ "grad_norm": 0.47481146454811096,
1108
+ "learning_rate": 0.000197109461702507,
1109
+ "loss": 0.8291,
1110
+ "step": 155
1111
+ },
1112
+ {
1113
+ "epoch": 0.33,
1114
+ "grad_norm": 0.1962246149778366,
1115
+ "learning_rate": 0.00019706965126527963,
1116
+ "loss": 0.7894,
1117
+ "step": 156
1118
+ },
1119
+ {
1120
+ "epoch": 0.33,
1121
+ "grad_norm": 1.366571307182312,
1122
+ "learning_rate": 0.00019702957262759965,
1123
+ "loss": 1.1808,
1124
+ "step": 157
1125
+ },
1126
+ {
1127
+ "epoch": 0.33,
1128
+ "grad_norm": 1.3261445760726929,
1129
+ "learning_rate": 0.00019698922590020312,
1130
+ "loss": 0.8769,
1131
+ "step": 158
1132
+ },
1133
+ {
1134
+ "epoch": 0.33,
1135
+ "grad_norm": 0.5637160539627075,
1136
+ "learning_rate": 0.00019694861119456679,
1137
+ "loss": 0.882,
1138
+ "step": 159
1139
+ },
1140
+ {
1141
+ "epoch": 0.34,
1142
+ "grad_norm": 0.4508800208568573,
1143
+ "learning_rate": 0.0001969077286229078,
1144
+ "loss": 1.2723,
1145
+ "step": 160
1146
+ },
1147
+ {
1148
+ "epoch": 0.34,
1149
+ "grad_norm": 0.41292956471443176,
1150
+ "learning_rate": 0.0001968665782981835,
1151
+ "loss": 0.7919,
1152
+ "step": 161
1153
+ },
1154
+ {
1155
+ "epoch": 0.34,
1156
+ "grad_norm": 0.6105634570121765,
1157
+ "learning_rate": 0.00019682516033409092,
1158
+ "loss": 1.0901,
1159
+ "step": 162
1160
+ },
1161
+ {
1162
+ "epoch": 0.34,
1163
+ "grad_norm": 0.6460319757461548,
1164
+ "learning_rate": 0.00019678347484506669,
1165
+ "loss": 1.0425,
1166
+ "step": 163
1167
+ },
1168
+ {
1169
+ "epoch": 0.35,
1170
+ "grad_norm": 0.8627430200576782,
1171
+ "learning_rate": 0.00019674152194628638,
1172
+ "loss": 0.8019,
1173
+ "step": 164
1174
+ },
1175
+ {
1176
+ "epoch": 0.35,
1177
+ "grad_norm": 0.3218872547149658,
1178
+ "learning_rate": 0.00019669930175366472,
1179
+ "loss": 0.8345,
1180
+ "step": 165
1181
+ },
1182
+ {
1183
+ "epoch": 0.35,
1184
+ "grad_norm": 0.6773053407669067,
1185
+ "learning_rate": 0.00019665681438385473,
1186
+ "loss": 1.3567,
1187
+ "step": 166
1188
+ },
1189
+ {
1190
+ "epoch": 0.35,
1191
+ "grad_norm": 0.3802971839904785,
1192
+ "learning_rate": 0.0001966140599542477,
1193
+ "loss": 0.7315,
1194
+ "step": 167
1195
+ },
1196
+ {
1197
+ "epoch": 0.35,
1198
+ "grad_norm": 0.9038891196250916,
1199
+ "learning_rate": 0.0001965710385829728,
1200
+ "loss": 0.6807,
1201
+ "step": 168
1202
+ },
1203
+ {
1204
+ "epoch": 0.36,
1205
+ "grad_norm": 0.7831525802612305,
1206
+ "learning_rate": 0.00019652775038889674,
1207
+ "loss": 1.2796,
1208
+ "step": 169
1209
+ },
1210
+ {
1211
+ "epoch": 0.36,
1212
+ "grad_norm": 0.3705346882343292,
1213
+ "learning_rate": 0.00019648419549162348,
1214
+ "loss": 0.8275,
1215
+ "step": 170
1216
+ },
1217
+ {
1218
+ "epoch": 0.36,
1219
+ "grad_norm": 0.7794845104217529,
1220
+ "learning_rate": 0.0001964403740114939,
1221
+ "loss": 0.7539,
1222
+ "step": 171
1223
+ },
1224
+ {
1225
+ "epoch": 0.36,
1226
+ "grad_norm": 0.2621815800666809,
1227
+ "learning_rate": 0.00019639628606958533,
1228
+ "loss": 0.976,
1229
+ "step": 172
1230
+ },
1231
+ {
1232
+ "epoch": 0.36,
1233
+ "grad_norm": 0.6929745674133301,
1234
+ "learning_rate": 0.00019635193178771143,
1235
+ "loss": 0.6198,
1236
+ "step": 173
1237
+ },
1238
+ {
1239
+ "epoch": 0.37,
1240
+ "grad_norm": 0.543230414390564,
1241
+ "learning_rate": 0.0001963073112884217,
1242
+ "loss": 0.9319,
1243
+ "step": 174
1244
+ },
1245
+ {
1246
+ "epoch": 0.37,
1247
+ "grad_norm": 0.6732174158096313,
1248
+ "learning_rate": 0.0001962624246950012,
1249
+ "loss": 0.804,
1250
+ "step": 175
1251
+ },
1252
+ {
1253
+ "epoch": 0.37,
1254
+ "grad_norm": 0.25452062487602234,
1255
+ "learning_rate": 0.00019621727213147027,
1256
+ "loss": 0.7632,
1257
+ "step": 176
1258
+ },
1259
+ {
1260
+ "epoch": 0.37,
1261
+ "grad_norm": 0.6591973304748535,
1262
+ "learning_rate": 0.00019617185372258392,
1263
+ "loss": 0.9745,
1264
+ "step": 177
1265
+ },
1266
+ {
1267
+ "epoch": 0.37,
1268
+ "grad_norm": 0.6275454163551331,
1269
+ "learning_rate": 0.0001961261695938319,
1270
+ "loss": 0.3411,
1271
+ "step": 178
1272
+ },
1273
+ {
1274
+ "epoch": 0.38,
1275
+ "grad_norm": 0.6691128611564636,
1276
+ "learning_rate": 0.00019608021987143804,
1277
+ "loss": 0.9564,
1278
+ "step": 179
1279
+ },
1280
+ {
1281
+ "epoch": 0.38,
1282
+ "grad_norm": 0.3190310299396515,
1283
+ "learning_rate": 0.00019603400468235998,
1284
+ "loss": 1.3002,
1285
+ "step": 180
1286
+ },
1287
+ {
1288
+ "epoch": 0.38,
1289
+ "grad_norm": 0.4648153781890869,
1290
+ "learning_rate": 0.0001959875241542889,
1291
+ "loss": 0.9507,
1292
+ "step": 181
1293
+ },
1294
+ {
1295
+ "epoch": 0.38,
1296
+ "grad_norm": 0.5921639800071716,
1297
+ "learning_rate": 0.00019594077841564907,
1298
+ "loss": 0.9397,
1299
+ "step": 182
1300
+ },
1301
+ {
1302
+ "epoch": 0.39,
1303
+ "grad_norm": 0.5769446492195129,
1304
+ "learning_rate": 0.00019589376759559745,
1305
+ "loss": 0.9958,
1306
+ "step": 183
1307
+ },
1308
+ {
1309
+ "epoch": 0.39,
1310
+ "grad_norm": 0.8454503417015076,
1311
+ "learning_rate": 0.00019584649182402357,
1312
+ "loss": 1.189,
1313
+ "step": 184
1314
+ },
1315
+ {
1316
+ "epoch": 0.39,
1317
+ "grad_norm": 0.2865101099014282,
1318
+ "learning_rate": 0.0001957989512315489,
1319
+ "loss": 0.6747,
1320
+ "step": 185
1321
+ },
1322
+ {
1323
+ "epoch": 0.39,
1324
+ "grad_norm": 0.3642055094242096,
1325
+ "learning_rate": 0.0001957511459495266,
1326
+ "loss": 0.5196,
1327
+ "step": 186
1328
+ },
1329
+ {
1330
+ "epoch": 0.39,
1331
+ "grad_norm": 0.4965610206127167,
1332
+ "learning_rate": 0.00019570307611004124,
1333
+ "loss": 0.9448,
1334
+ "step": 187
1335
+ },
1336
+ {
1337
+ "epoch": 0.4,
1338
+ "grad_norm": 0.5694214105606079,
1339
+ "learning_rate": 0.00019565474184590826,
1340
+ "loss": 0.868,
1341
+ "step": 188
1342
+ },
1343
+ {
1344
+ "epoch": 0.4,
1345
+ "grad_norm": 0.6402484774589539,
1346
+ "learning_rate": 0.00019560614329067378,
1347
+ "loss": 0.8872,
1348
+ "step": 189
1349
+ },
1350
+ {
1351
+ "epoch": 0.4,
1352
+ "grad_norm": 0.37722048163414,
1353
+ "learning_rate": 0.0001955572805786141,
1354
+ "loss": 0.9253,
1355
+ "step": 190
1356
+ },
1357
+ {
1358
+ "epoch": 0.4,
1359
+ "grad_norm": 1.9157966375350952,
1360
+ "learning_rate": 0.00019550815384473534,
1361
+ "loss": 1.6508,
1362
+ "step": 191
1363
+ },
1364
+ {
1365
+ "epoch": 0.4,
1366
+ "grad_norm": 0.33376675844192505,
1367
+ "learning_rate": 0.0001954587632247732,
1368
+ "loss": 0.9109,
1369
+ "step": 192
1370
+ },
1371
+ {
1372
+ "epoch": 0.41,
1373
+ "grad_norm": 0.2680880129337311,
1374
+ "learning_rate": 0.00019540910885519242,
1375
+ "loss": 1.0693,
1376
+ "step": 193
1377
+ },
1378
+ {
1379
+ "epoch": 0.41,
1380
+ "grad_norm": 0.7726811766624451,
1381
+ "learning_rate": 0.00019535919087318652,
1382
+ "loss": 0.9574,
1383
+ "step": 194
1384
+ },
1385
+ {
1386
+ "epoch": 0.41,
1387
+ "grad_norm": 0.8604207634925842,
1388
+ "learning_rate": 0.0001953090094166773,
1389
+ "loss": 0.9475,
1390
+ "step": 195
1391
+ },
1392
+ {
1393
+ "epoch": 0.41,
1394
+ "grad_norm": 1.3954675197601318,
1395
+ "learning_rate": 0.0001952585646243146,
1396
+ "loss": 1.5094,
1397
+ "step": 196
1398
+ },
1399
+ {
1400
+ "epoch": 0.41,
1401
+ "grad_norm": 0.39931145310401917,
1402
+ "learning_rate": 0.00019520785663547586,
1403
+ "loss": 0.9915,
1404
+ "step": 197
1405
+ },
1406
+ {
1407
+ "epoch": 0.42,
1408
+ "grad_norm": 0.772156298160553,
1409
+ "learning_rate": 0.00019515688559026563,
1410
+ "loss": 1.4155,
1411
+ "step": 198
1412
+ },
1413
+ {
1414
+ "epoch": 0.42,
1415
+ "grad_norm": 0.48633861541748047,
1416
+ "learning_rate": 0.00019510565162951537,
1417
+ "loss": 0.9607,
1418
+ "step": 199
1419
+ },
1420
+ {
1421
+ "epoch": 0.42,
1422
+ "grad_norm": 0.4661516845226288,
1423
+ "learning_rate": 0.0001950541548947829,
1424
+ "loss": 1.0283,
1425
+ "step": 200
1426
+ },
1427
+ {
1428
+ "epoch": 0.42,
1429
+ "grad_norm": 0.8846752047538757,
1430
+ "learning_rate": 0.00019500239552835215,
1431
+ "loss": 0.756,
1432
+ "step": 201
1433
+ },
1434
+ {
1435
+ "epoch": 0.43,
1436
+ "grad_norm": 0.9870714545249939,
1437
+ "learning_rate": 0.00019495037367323262,
1438
+ "loss": 0.7688,
1439
+ "step": 202
1440
+ },
1441
+ {
1442
+ "epoch": 0.43,
1443
+ "grad_norm": 0.7435501217842102,
1444
+ "learning_rate": 0.00019489808947315915,
1445
+ "loss": 0.4752,
1446
+ "step": 203
1447
+ },
1448
+ {
1449
+ "epoch": 0.43,
1450
+ "grad_norm": 0.6509325504302979,
1451
+ "learning_rate": 0.0001948455430725913,
1452
+ "loss": 0.9053,
1453
+ "step": 204
1454
+ },
1455
+ {
1456
+ "epoch": 0.43,
1457
+ "grad_norm": 0.30190637707710266,
1458
+ "learning_rate": 0.0001947927346167132,
1459
+ "loss": 0.9323,
1460
+ "step": 205
1461
+ },
1462
+ {
1463
+ "epoch": 0.43,
1464
+ "grad_norm": 0.420055627822876,
1465
+ "learning_rate": 0.00019473966425143292,
1466
+ "loss": 0.6446,
1467
+ "step": 206
1468
+ },
1469
+ {
1470
+ "epoch": 0.44,
1471
+ "grad_norm": 0.49513018131256104,
1472
+ "learning_rate": 0.00019468633212338233,
1473
+ "loss": 0.9022,
1474
+ "step": 207
1475
+ },
1476
+ {
1477
+ "epoch": 0.44,
1478
+ "grad_norm": 0.4812709391117096,
1479
+ "learning_rate": 0.00019463273837991643,
1480
+ "loss": 0.6835,
1481
+ "step": 208
1482
+ },
1483
+ {
1484
+ "epoch": 0.44,
1485
+ "grad_norm": 0.2101246416568756,
1486
+ "learning_rate": 0.00019457888316911306,
1487
+ "loss": 0.5991,
1488
+ "step": 209
1489
+ },
1490
+ {
1491
+ "epoch": 0.44,
1492
+ "grad_norm": 0.3539298176765442,
1493
+ "learning_rate": 0.00019452476663977248,
1494
+ "loss": 0.7323,
1495
+ "step": 210
1496
+ },
1497
+ {
1498
+ "epoch": 0.44,
1499
+ "grad_norm": 0.29954612255096436,
1500
+ "learning_rate": 0.00019447038894141705,
1501
+ "loss": 0.6868,
1502
+ "step": 211
1503
+ },
1504
+ {
1505
+ "epoch": 0.45,
1506
+ "grad_norm": 0.4053567349910736,
1507
+ "learning_rate": 0.00019441575022429065,
1508
+ "loss": 1.0805,
1509
+ "step": 212
1510
+ },
1511
+ {
1512
+ "epoch": 0.45,
1513
+ "grad_norm": 0.7733739614486694,
1514
+ "learning_rate": 0.00019436085063935835,
1515
+ "loss": 1.3524,
1516
+ "step": 213
1517
+ },
1518
+ {
1519
+ "epoch": 0.45,
1520
+ "grad_norm": 0.6096423864364624,
1521
+ "learning_rate": 0.00019430569033830605,
1522
+ "loss": 1.0183,
1523
+ "step": 214
1524
+ },
1525
+ {
1526
+ "epoch": 0.45,
1527
+ "grad_norm": 1.1940584182739258,
1528
+ "learning_rate": 0.00019425026947353992,
1529
+ "loss": 1.0919,
1530
+ "step": 215
1531
+ },
1532
+ {
1533
+ "epoch": 0.45,
1534
+ "grad_norm": 0.4030895233154297,
1535
+ "learning_rate": 0.00019419458819818614,
1536
+ "loss": 0.7642,
1537
+ "step": 216
1538
+ },
1539
+ {
1540
+ "epoch": 0.46,
1541
+ "grad_norm": 1.4116997718811035,
1542
+ "learning_rate": 0.00019413864666609034,
1543
+ "loss": 0.6112,
1544
+ "step": 217
1545
+ },
1546
+ {
1547
+ "epoch": 0.46,
1548
+ "grad_norm": 0.4545953869819641,
1549
+ "learning_rate": 0.00019408244503181724,
1550
+ "loss": 0.7328,
1551
+ "step": 218
1552
+ },
1553
+ {
1554
+ "epoch": 0.46,
1555
+ "grad_norm": 0.9334838390350342,
1556
+ "learning_rate": 0.0001940259834506502,
1557
+ "loss": 1.0518,
1558
+ "step": 219
1559
+ },
1560
+ {
1561
+ "epoch": 0.46,
1562
+ "grad_norm": 0.2695348858833313,
1563
+ "learning_rate": 0.00019396926207859084,
1564
+ "loss": 0.987,
1565
+ "step": 220
1566
+ },
1567
+ {
1568
+ "epoch": 0.47,
1569
+ "grad_norm": 1.3967281579971313,
1570
+ "learning_rate": 0.00019391228107235858,
1571
+ "loss": 1.0819,
1572
+ "step": 221
1573
+ },
1574
+ {
1575
+ "epoch": 0.47,
1576
+ "grad_norm": 1.0220236778259277,
1577
+ "learning_rate": 0.00019385504058939024,
1578
+ "loss": 0.9621,
1579
+ "step": 222
1580
+ },
1581
+ {
1582
+ "epoch": 0.47,
1583
+ "grad_norm": 2.5694682598114014,
1584
+ "learning_rate": 0.00019379754078783937,
1585
+ "loss": 1.0647,
1586
+ "step": 223
1587
+ },
1588
+ {
1589
+ "epoch": 0.47,
1590
+ "grad_norm": 0.6181725263595581,
1591
+ "learning_rate": 0.00019373978182657625,
1592
+ "loss": 1.0991,
1593
+ "step": 224
1594
+ },
1595
+ {
1596
+ "epoch": 0.47,
1597
+ "grad_norm": 0.508532702922821,
1598
+ "learning_rate": 0.0001936817638651871,
1599
+ "loss": 1.0276,
1600
+ "step": 225
1601
+ },
1602
+ {
1603
+ "epoch": 0.48,
1604
+ "grad_norm": 0.3763074278831482,
1605
+ "learning_rate": 0.00019362348706397373,
1606
+ "loss": 0.7447,
1607
+ "step": 226
1608
+ },
1609
+ {
1610
+ "epoch": 0.48,
1611
+ "grad_norm": 0.9533042311668396,
1612
+ "learning_rate": 0.00019356495158395315,
1613
+ "loss": 1.1979,
1614
+ "step": 227
1615
+ },
1616
+ {
1617
+ "epoch": 0.48,
1618
+ "grad_norm": 0.43593689799308777,
1619
+ "learning_rate": 0.00019350615758685708,
1620
+ "loss": 1.0028,
1621
+ "step": 228
1622
+ },
1623
+ {
1624
+ "epoch": 0.48,
1625
+ "grad_norm": 0.7646205425262451,
1626
+ "learning_rate": 0.00019344710523513156,
1627
+ "loss": 1.463,
1628
+ "step": 229
1629
+ },
1630
+ {
1631
+ "epoch": 0.48,
1632
+ "grad_norm": 0.29402196407318115,
1633
+ "learning_rate": 0.00019338779469193639,
1634
+ "loss": 1.2726,
1635
+ "step": 230
1636
+ },
1637
+ {
1638
+ "epoch": 0.49,
1639
+ "grad_norm": 0.5773300528526306,
1640
+ "learning_rate": 0.00019332822612114475,
1641
+ "loss": 0.4847,
1642
+ "step": 231
1643
+ },
1644
+ {
1645
+ "epoch": 0.49,
1646
+ "grad_norm": 1.0580178499221802,
1647
+ "learning_rate": 0.00019326839968734279,
1648
+ "loss": 1.0639,
1649
+ "step": 232
1650
+ },
1651
+ {
1652
+ "epoch": 0.49,
1653
+ "grad_norm": 0.6212771534919739,
1654
+ "learning_rate": 0.00019320831555582908,
1655
+ "loss": 0.7302,
1656
+ "step": 233
1657
+ },
1658
+ {
1659
+ "epoch": 0.49,
1660
+ "grad_norm": 1.1953450441360474,
1661
+ "learning_rate": 0.00019314797389261424,
1662
+ "loss": 0.9873,
1663
+ "step": 234
1664
+ },
1665
+ {
1666
+ "epoch": 0.49,
1667
+ "grad_norm": 1.856995940208435,
1668
+ "learning_rate": 0.00019308737486442045,
1669
+ "loss": 0.9573,
1670
+ "step": 235
1671
+ },
1672
+ {
1673
+ "epoch": 0.5,
1674
+ "grad_norm": 0.36539939045906067,
1675
+ "learning_rate": 0.00019302651863868092,
1676
+ "loss": 0.6884,
1677
+ "step": 236
1678
+ },
1679
+ {
1680
+ "epoch": 0.5,
1681
+ "grad_norm": 0.3269266188144684,
1682
+ "learning_rate": 0.0001929654053835395,
1683
+ "loss": 0.9445,
1684
+ "step": 237
1685
+ },
1686
+ {
1687
+ "epoch": 0.5,
1688
+ "grad_norm": 0.46403074264526367,
1689
+ "learning_rate": 0.00019290403526785025,
1690
+ "loss": 0.9783,
1691
+ "step": 238
1692
+ },
1693
+ {
1694
+ "epoch": 0.5,
1695
+ "eval_loss": 0.8782849311828613,
1696
+ "eval_runtime": 61.3598,
1697
+ "eval_samples_per_second": 1.63,
1698
+ "eval_steps_per_second": 1.63,
1699
+ "step": 238
1700
+ },
1701
+ {
1702
+ "epoch": 0.5,
1703
+ "grad_norm": 0.6627680659294128,
1704
+ "learning_rate": 0.00019284240846117697,
1705
+ "loss": 0.9527,
1706
+ "step": 239
1707
+ },
1708
+ {
1709
+ "epoch": 0.51,
1710
+ "grad_norm": 0.866802990436554,
1711
+ "learning_rate": 0.00019278052513379255,
1712
+ "loss": 0.6096,
1713
+ "step": 240
1714
+ },
1715
+ {
1716
+ "epoch": 0.51,
1717
+ "grad_norm": 0.5304962396621704,
1718
+ "learning_rate": 0.00019271838545667876,
1719
+ "loss": 0.8335,
1720
+ "step": 241
1721
+ },
1722
+ {
1723
+ "epoch": 0.51,
1724
+ "grad_norm": 1.076063632965088,
1725
+ "learning_rate": 0.00019265598960152555,
1726
+ "loss": 1.3308,
1727
+ "step": 242
1728
+ },
1729
+ {
1730
+ "epoch": 0.51,
1731
+ "grad_norm": 2.491516351699829,
1732
+ "learning_rate": 0.00019259333774073083,
1733
+ "loss": 1.4458,
1734
+ "step": 243
1735
+ },
1736
+ {
1737
+ "epoch": 0.51,
1738
+ "grad_norm": 1.3771064281463623,
1739
+ "learning_rate": 0.00019253043004739968,
1740
+ "loss": 1.4581,
1741
+ "step": 244
1742
+ },
1743
+ {
1744
+ "epoch": 0.52,
1745
+ "grad_norm": 0.24413131177425385,
1746
+ "learning_rate": 0.00019246726669534415,
1747
+ "loss": 0.7537,
1748
+ "step": 245
1749
+ },
1750
+ {
1751
+ "epoch": 0.52,
1752
+ "grad_norm": 1.02517831325531,
1753
+ "learning_rate": 0.00019240384785908265,
1754
+ "loss": 1.0646,
1755
+ "step": 246
1756
+ },
1757
+ {
1758
+ "epoch": 0.52,
1759
+ "grad_norm": 0.4848421514034271,
1760
+ "learning_rate": 0.00019234017371383945,
1761
+ "loss": 0.6972,
1762
+ "step": 247
1763
+ },
1764
+ {
1765
+ "epoch": 0.52,
1766
+ "grad_norm": 0.8870792388916016,
1767
+ "learning_rate": 0.00019227624443554425,
1768
+ "loss": 1.2114,
1769
+ "step": 248
1770
+ },
1771
+ {
1772
+ "epoch": 0.52,
1773
+ "grad_norm": 0.5171313285827637,
1774
+ "learning_rate": 0.00019221206020083166,
1775
+ "loss": 0.7243,
1776
+ "step": 249
1777
+ },
1778
+ {
1779
+ "epoch": 0.53,
1780
+ "grad_norm": 0.5975112915039062,
1781
+ "learning_rate": 0.00019214762118704076,
1782
+ "loss": 0.964,
1783
+ "step": 250
1784
+ },
1785
+ {
1786
+ "epoch": 0.53,
1787
+ "grad_norm": 1.0921701192855835,
1788
+ "learning_rate": 0.0001920829275722146,
1789
+ "loss": 1.1413,
1790
+ "step": 251
1791
+ },
1792
+ {
1793
+ "epoch": 0.53,
1794
+ "grad_norm": 0.6540035009384155,
1795
+ "learning_rate": 0.00019201797953509955,
1796
+ "loss": 0.9732,
1797
+ "step": 252
1798
+ },
1799
+ {
1800
+ "epoch": 0.53,
1801
+ "grad_norm": 1.137863278388977,
1802
+ "learning_rate": 0.0001919527772551451,
1803
+ "loss": 1.3374,
1804
+ "step": 253
1805
+ },
1806
+ {
1807
+ "epoch": 0.53,
1808
+ "grad_norm": 1.4139158725738525,
1809
+ "learning_rate": 0.00019188732091250307,
1810
+ "loss": 1.1147,
1811
+ "step": 254
1812
+ },
1813
+ {
1814
+ "epoch": 0.54,
1815
+ "grad_norm": 0.5039550065994263,
1816
+ "learning_rate": 0.00019182161068802741,
1817
+ "loss": 0.7832,
1818
+ "step": 255
1819
+ },
1820
+ {
1821
+ "epoch": 0.54,
1822
+ "grad_norm": 1.567670464515686,
1823
+ "learning_rate": 0.00019175564676327339,
1824
+ "loss": 0.6684,
1825
+ "step": 256
1826
+ },
1827
+ {
1828
+ "epoch": 0.54,
1829
+ "grad_norm": 0.4372114837169647,
1830
+ "learning_rate": 0.0001916894293204973,
1831
+ "loss": 0.7285,
1832
+ "step": 257
1833
+ },
1834
+ {
1835
+ "epoch": 0.54,
1836
+ "grad_norm": 0.4466225206851959,
1837
+ "learning_rate": 0.00019162295854265594,
1838
+ "loss": 0.5705,
1839
+ "step": 258
1840
+ },
1841
+ {
1842
+ "epoch": 0.55,
1843
+ "grad_norm": 1.7975250482559204,
1844
+ "learning_rate": 0.00019155623461340594,
1845
+ "loss": 1.4155,
1846
+ "step": 259
1847
+ },
1848
+ {
1849
+ "epoch": 0.55,
1850
+ "grad_norm": 0.6310514211654663,
1851
+ "learning_rate": 0.00019148925771710347,
1852
+ "loss": 0.7388,
1853
+ "step": 260
1854
+ },
1855
+ {
1856
+ "epoch": 0.55,
1857
+ "grad_norm": 0.5273220539093018,
1858
+ "learning_rate": 0.0001914220280388037,
1859
+ "loss": 0.9241,
1860
+ "step": 261
1861
+ },
1862
+ {
1863
+ "epoch": 0.55,
1864
+ "grad_norm": 1.8354101181030273,
1865
+ "learning_rate": 0.0001913545457642601,
1866
+ "loss": 0.8085,
1867
+ "step": 262
1868
+ },
1869
+ {
1870
+ "epoch": 0.55,
1871
+ "grad_norm": 0.7362698316574097,
1872
+ "learning_rate": 0.00019128681107992415,
1873
+ "loss": 0.953,
1874
+ "step": 263
1875
+ },
1876
+ {
1877
+ "epoch": 0.56,
1878
+ "grad_norm": 0.5334580540657043,
1879
+ "learning_rate": 0.00019121882417294462,
1880
+ "loss": 0.4416,
1881
+ "step": 264
1882
+ },
1883
+ {
1884
+ "epoch": 0.56,
1885
+ "grad_norm": 0.6351854205131531,
1886
+ "learning_rate": 0.00019115058523116733,
1887
+ "loss": 0.6414,
1888
+ "step": 265
1889
+ },
1890
+ {
1891
+ "epoch": 0.56,
1892
+ "grad_norm": 0.28386977314949036,
1893
+ "learning_rate": 0.00019108209444313433,
1894
+ "loss": 1.0273,
1895
+ "step": 266
1896
+ },
1897
+ {
1898
+ "epoch": 0.56,
1899
+ "grad_norm": 0.5504246354103088,
1900
+ "learning_rate": 0.00019101335199808354,
1901
+ "loss": 1.1191,
1902
+ "step": 267
1903
+ },
1904
+ {
1905
+ "epoch": 0.56,
1906
+ "grad_norm": 0.7449864149093628,
1907
+ "learning_rate": 0.00019094435808594823,
1908
+ "loss": 1.1073,
1909
+ "step": 268
1910
+ },
1911
+ {
1912
+ "epoch": 0.57,
1913
+ "grad_norm": 0.6302490830421448,
1914
+ "learning_rate": 0.00019087511289735644,
1915
+ "loss": 1.2092,
1916
+ "step": 269
1917
+ },
1918
+ {
1919
+ "epoch": 0.57,
1920
+ "grad_norm": 0.5618910789489746,
1921
+ "learning_rate": 0.0001908056166236305,
1922
+ "loss": 1.1966,
1923
+ "step": 270
1924
+ },
1925
+ {
1926
+ "epoch": 0.57,
1927
+ "grad_norm": 0.46393775939941406,
1928
+ "learning_rate": 0.0001907358694567865,
1929
+ "loss": 0.7148,
1930
+ "step": 271
1931
+ },
1932
+ {
1933
+ "epoch": 0.57,
1934
+ "grad_norm": 0.34640607237815857,
1935
+ "learning_rate": 0.00019066587158953366,
1936
+ "loss": 1.1297,
1937
+ "step": 272
1938
+ },
1939
+ {
1940
+ "epoch": 0.57,
1941
+ "grad_norm": 1.3277580738067627,
1942
+ "learning_rate": 0.00019059562321527396,
1943
+ "loss": 1.0978,
1944
+ "step": 273
1945
+ },
1946
+ {
1947
+ "epoch": 0.58,
1948
+ "grad_norm": 0.8730579018592834,
1949
+ "learning_rate": 0.0001905251245281015,
1950
+ "loss": 0.9732,
1951
+ "step": 274
1952
+ },
1953
+ {
1954
+ "epoch": 0.58,
1955
+ "grad_norm": 0.32950034737586975,
1956
+ "learning_rate": 0.00019045437572280194,
1957
+ "loss": 1.0795,
1958
+ "step": 275
1959
+ },
1960
+ {
1961
+ "epoch": 0.58,
1962
+ "grad_norm": 0.48170116543769836,
1963
+ "learning_rate": 0.00019038337699485208,
1964
+ "loss": 0.8124,
1965
+ "step": 276
1966
+ },
1967
+ {
1968
+ "epoch": 0.58,
1969
+ "grad_norm": 0.858323335647583,
1970
+ "learning_rate": 0.00019031212854041918,
1971
+ "loss": 0.813,
1972
+ "step": 277
1973
+ },
1974
+ {
1975
+ "epoch": 0.59,
1976
+ "grad_norm": 0.9366027116775513,
1977
+ "learning_rate": 0.00019024063055636057,
1978
+ "loss": 1.5074,
1979
+ "step": 278
1980
+ },
1981
+ {
1982
+ "epoch": 0.59,
1983
+ "grad_norm": 0.4378308653831482,
1984
+ "learning_rate": 0.00019016888324022296,
1985
+ "loss": 0.8387,
1986
+ "step": 279
1987
+ },
1988
+ {
1989
+ "epoch": 0.59,
1990
+ "grad_norm": 0.5781106948852539,
1991
+ "learning_rate": 0.0001900968867902419,
1992
+ "loss": 1.0496,
1993
+ "step": 280
1994
+ },
1995
+ {
1996
+ "epoch": 0.59,
1997
+ "grad_norm": 0.834186851978302,
1998
+ "learning_rate": 0.00019002464140534147,
1999
+ "loss": 1.2684,
2000
+ "step": 281
2001
+ },
2002
+ {
2003
+ "epoch": 0.59,
2004
+ "grad_norm": 0.752008855342865,
2005
+ "learning_rate": 0.00018995214728513343,
2006
+ "loss": 1.069,
2007
+ "step": 282
2008
+ },
2009
+ {
2010
+ "epoch": 0.6,
2011
+ "grad_norm": 0.3941871225833893,
2012
+ "learning_rate": 0.0001898794046299167,
2013
+ "loss": 0.942,
2014
+ "step": 283
2015
+ },
2016
+ {
2017
+ "epoch": 0.6,
2018
+ "grad_norm": 0.4069131314754486,
2019
+ "learning_rate": 0.0001898064136406771,
2020
+ "loss": 0.7116,
2021
+ "step": 284
2022
+ },
2023
+ {
2024
+ "epoch": 0.6,
2025
+ "grad_norm": 0.6478765606880188,
2026
+ "learning_rate": 0.00018973317451908642,
2027
+ "loss": 0.9494,
2028
+ "step": 285
2029
+ },
2030
+ {
2031
+ "epoch": 0.6,
2032
+ "grad_norm": 1.8658535480499268,
2033
+ "learning_rate": 0.0001896596874675021,
2034
+ "loss": 0.7592,
2035
+ "step": 286
2036
+ },
2037
+ {
2038
+ "epoch": 0.6,
2039
+ "grad_norm": 0.8622011542320251,
2040
+ "learning_rate": 0.0001895859526889666,
2041
+ "loss": 0.9392,
2042
+ "step": 287
2043
+ },
2044
+ {
2045
+ "epoch": 0.61,
2046
+ "grad_norm": 0.8127020001411438,
2047
+ "learning_rate": 0.00018951197038720688,
2048
+ "loss": 1.3309,
2049
+ "step": 288
2050
+ },
2051
+ {
2052
+ "epoch": 0.61,
2053
+ "grad_norm": 0.5042945146560669,
2054
+ "learning_rate": 0.0001894377407666337,
2055
+ "loss": 0.7607,
2056
+ "step": 289
2057
+ },
2058
+ {
2059
+ "epoch": 0.61,
2060
+ "grad_norm": 0.7252426743507385,
2061
+ "learning_rate": 0.00018936326403234125,
2062
+ "loss": 1.1221,
2063
+ "step": 290
2064
+ },
2065
+ {
2066
+ "epoch": 0.61,
2067
+ "grad_norm": 0.7334456443786621,
2068
+ "learning_rate": 0.0001892885403901064,
2069
+ "loss": 0.4738,
2070
+ "step": 291
2071
+ },
2072
+ {
2073
+ "epoch": 0.61,
2074
+ "grad_norm": 2.6204662322998047,
2075
+ "learning_rate": 0.00018921357004638835,
2076
+ "loss": 1.2511,
2077
+ "step": 292
2078
+ },
2079
+ {
2080
+ "epoch": 0.62,
2081
+ "grad_norm": 0.5708286762237549,
2082
+ "learning_rate": 0.00018913835320832778,
2083
+ "loss": 1.0887,
2084
+ "step": 293
2085
+ },
2086
+ {
2087
+ "epoch": 0.62,
2088
+ "grad_norm": 1.0324314832687378,
2089
+ "learning_rate": 0.00018906289008374655,
2090
+ "loss": 1.1019,
2091
+ "step": 294
2092
+ },
2093
+ {
2094
+ "epoch": 0.62,
2095
+ "grad_norm": 0.3663407862186432,
2096
+ "learning_rate": 0.0001889871808811469,
2097
+ "loss": 1.0333,
2098
+ "step": 295
2099
+ },
2100
+ {
2101
+ "epoch": 0.62,
2102
+ "grad_norm": 0.7219849824905396,
2103
+ "learning_rate": 0.00018891122580971098,
2104
+ "loss": 0.858,
2105
+ "step": 296
2106
+ },
2107
+ {
2108
+ "epoch": 0.63,
2109
+ "grad_norm": 0.7850363850593567,
2110
+ "learning_rate": 0.00018883502507930042,
2111
+ "loss": 0.9503,
2112
+ "step": 297
2113
+ },
2114
+ {
2115
+ "epoch": 0.63,
2116
+ "grad_norm": 0.28012195229530334,
2117
+ "learning_rate": 0.00018875857890045543,
2118
+ "loss": 0.8068,
2119
+ "step": 298
2120
+ },
2121
+ {
2122
+ "epoch": 0.63,
2123
+ "grad_norm": 0.7574068307876587,
2124
+ "learning_rate": 0.00018868188748439444,
2125
+ "loss": 0.7557,
2126
+ "step": 299
2127
+ },
2128
+ {
2129
+ "epoch": 0.63,
2130
+ "grad_norm": 0.9131019711494446,
2131
+ "learning_rate": 0.00018860495104301345,
2132
+ "loss": 1.1462,
2133
+ "step": 300
2134
+ },
2135
+ {
2136
+ "epoch": 0.63,
2137
+ "grad_norm": 0.24085545539855957,
2138
+ "learning_rate": 0.00018852776978888551,
2139
+ "loss": 0.8286,
2140
+ "step": 301
2141
+ },
2142
+ {
2143
+ "epoch": 0.64,
2144
+ "grad_norm": 0.4502617418766022,
2145
+ "learning_rate": 0.00018845034393526005,
2146
+ "loss": 1.0052,
2147
+ "step": 302
2148
+ },
2149
+ {
2150
+ "epoch": 0.64,
2151
+ "grad_norm": 0.7258254289627075,
2152
+ "learning_rate": 0.00018837267369606228,
2153
+ "loss": 0.9703,
2154
+ "step": 303
2155
+ },
2156
+ {
2157
+ "epoch": 0.64,
2158
+ "grad_norm": 0.6078888773918152,
2159
+ "learning_rate": 0.00018829475928589271,
2160
+ "loss": 0.8479,
2161
+ "step": 304
2162
+ },
2163
+ {
2164
+ "epoch": 0.64,
2165
+ "grad_norm": 0.5912296772003174,
2166
+ "learning_rate": 0.00018821660092002641,
2167
+ "loss": 1.0336,
2168
+ "step": 305
2169
+ },
2170
+ {
2171
+ "epoch": 0.64,
2172
+ "grad_norm": 0.3440995216369629,
2173
+ "learning_rate": 0.0001881381988144126,
2174
+ "loss": 0.7629,
2175
+ "step": 306
2176
+ },
2177
+ {
2178
+ "epoch": 0.65,
2179
+ "grad_norm": 0.5613306164741516,
2180
+ "learning_rate": 0.0001880595531856738,
2181
+ "loss": 1.0355,
2182
+ "step": 307
2183
+ },
2184
+ {
2185
+ "epoch": 0.65,
2186
+ "grad_norm": 0.5265874862670898,
2187
+ "learning_rate": 0.0001879806642511055,
2188
+ "loss": 0.9046,
2189
+ "step": 308
2190
+ },
2191
+ {
2192
+ "epoch": 0.65,
2193
+ "grad_norm": 0.37300053238868713,
2194
+ "learning_rate": 0.0001879015322286754,
2195
+ "loss": 0.578,
2196
+ "step": 309
2197
+ },
2198
+ {
2199
+ "epoch": 0.65,
2200
+ "grad_norm": 0.7948945164680481,
2201
+ "learning_rate": 0.00018782215733702286,
2202
+ "loss": 0.5693,
2203
+ "step": 310
2204
+ },
2205
+ {
2206
+ "epoch": 0.65,
2207
+ "grad_norm": 0.5222792625427246,
2208
+ "learning_rate": 0.0001877425397954582,
2209
+ "loss": 0.812,
2210
+ "step": 311
2211
+ },
2212
+ {
2213
+ "epoch": 0.66,
2214
+ "grad_norm": 0.6407319903373718,
2215
+ "learning_rate": 0.00018766267982396224,
2216
+ "loss": 0.7317,
2217
+ "step": 312
2218
+ },
2219
+ {
2220
+ "epoch": 0.66,
2221
+ "grad_norm": 0.36041396856307983,
2222
+ "learning_rate": 0.00018758257764318567,
2223
+ "loss": 0.3617,
2224
+ "step": 313
2225
+ },
2226
+ {
2227
+ "epoch": 0.66,
2228
+ "grad_norm": 0.6465966105461121,
2229
+ "learning_rate": 0.00018750223347444828,
2230
+ "loss": 0.6037,
2231
+ "step": 314
2232
+ },
2233
+ {
2234
+ "epoch": 0.66,
2235
+ "grad_norm": 0.4281207025051117,
2236
+ "learning_rate": 0.00018742164753973855,
2237
+ "loss": 0.5269,
2238
+ "step": 315
2239
+ },
2240
+ {
2241
+ "epoch": 0.67,
2242
+ "grad_norm": 0.3671799898147583,
2243
+ "learning_rate": 0.00018734082006171299,
2244
+ "loss": 0.66,
2245
+ "step": 316
2246
+ },
2247
+ {
2248
+ "epoch": 0.67,
2249
+ "grad_norm": 0.4369129240512848,
2250
+ "learning_rate": 0.00018725975126369535,
2251
+ "loss": 1.1395,
2252
+ "step": 317
2253
+ },
2254
+ {
2255
+ "epoch": 0.67,
2256
+ "grad_norm": 0.4631548523902893,
2257
+ "learning_rate": 0.00018717844136967624,
2258
+ "loss": 0.7871,
2259
+ "step": 318
2260
+ },
2261
+ {
2262
+ "epoch": 0.67,
2263
+ "grad_norm": 0.4736942946910858,
2264
+ "learning_rate": 0.00018709689060431242,
2265
+ "loss": 1.2983,
2266
+ "step": 319
2267
+ },
2268
+ {
2269
+ "epoch": 0.67,
2270
+ "grad_norm": 0.7346480488777161,
2271
+ "learning_rate": 0.00018701509919292613,
2272
+ "loss": 0.9507,
2273
+ "step": 320
2274
+ },
2275
+ {
2276
+ "epoch": 0.68,
2277
+ "grad_norm": 0.5298660397529602,
2278
+ "learning_rate": 0.00018693306736150444,
2279
+ "loss": 0.6621,
2280
+ "step": 321
2281
+ },
2282
+ {
2283
+ "epoch": 0.68,
2284
+ "grad_norm": 0.5501769781112671,
2285
+ "learning_rate": 0.0001868507953366989,
2286
+ "loss": 0.6954,
2287
+ "step": 322
2288
+ },
2289
+ {
2290
+ "epoch": 0.68,
2291
+ "grad_norm": 1.565510630607605,
2292
+ "learning_rate": 0.0001867682833458245,
2293
+ "loss": 1.2279,
2294
+ "step": 323
2295
+ },
2296
+ {
2297
+ "epoch": 0.68,
2298
+ "grad_norm": 0.2679019570350647,
2299
+ "learning_rate": 0.00018668553161685933,
2300
+ "loss": 0.6207,
2301
+ "step": 324
2302
+ },
2303
+ {
2304
+ "epoch": 0.68,
2305
+ "grad_norm": 1.0185893774032593,
2306
+ "learning_rate": 0.00018660254037844388,
2307
+ "loss": 1.1179,
2308
+ "step": 325
2309
+ },
2310
+ {
2311
+ "epoch": 0.69,
2312
+ "grad_norm": 0.400493323802948,
2313
+ "learning_rate": 0.00018651930985988036,
2314
+ "loss": 0.5947,
2315
+ "step": 326
2316
+ },
2317
+ {
2318
+ "epoch": 0.69,
2319
+ "grad_norm": 0.7746186256408691,
2320
+ "learning_rate": 0.00018643584029113215,
2321
+ "loss": 1.0365,
2322
+ "step": 327
2323
+ },
2324
+ {
2325
+ "epoch": 0.69,
2326
+ "grad_norm": 0.5792235136032104,
2327
+ "learning_rate": 0.0001863521319028231,
2328
+ "loss": 0.7102,
2329
+ "step": 328
2330
+ },
2331
+ {
2332
+ "epoch": 0.69,
2333
+ "grad_norm": 0.35895833373069763,
2334
+ "learning_rate": 0.00018626818492623688,
2335
+ "loss": 0.5571,
2336
+ "step": 329
2337
+ },
2338
+ {
2339
+ "epoch": 0.69,
2340
+ "grad_norm": 0.41158926486968994,
2341
+ "learning_rate": 0.0001861839995933164,
2342
+ "loss": 0.9009,
2343
+ "step": 330
2344
+ },
2345
+ {
2346
+ "epoch": 0.7,
2347
+ "grad_norm": 0.5845640301704407,
2348
+ "learning_rate": 0.00018609957613666315,
2349
+ "loss": 0.3317,
2350
+ "step": 331
2351
+ },
2352
+ {
2353
+ "epoch": 0.7,
2354
+ "grad_norm": 0.4458400309085846,
2355
+ "learning_rate": 0.00018601491478953657,
2356
+ "loss": 1.0094,
2357
+ "step": 332
2358
+ },
2359
+ {
2360
+ "epoch": 0.7,
2361
+ "grad_norm": 0.6415822505950928,
2362
+ "learning_rate": 0.00018593001578585326,
2363
+ "loss": 0.9772,
2364
+ "step": 333
2365
+ },
2366
+ {
2367
+ "epoch": 0.7,
2368
+ "grad_norm": 1.616220474243164,
2369
+ "learning_rate": 0.00018584487936018661,
2370
+ "loss": 0.6879,
2371
+ "step": 334
2372
+ },
2373
+ {
2374
+ "epoch": 0.71,
2375
+ "grad_norm": 1.4885902404785156,
2376
+ "learning_rate": 0.00018575950574776595,
2377
+ "loss": 0.9627,
2378
+ "step": 335
2379
+ },
2380
+ {
2381
+ "epoch": 0.71,
2382
+ "grad_norm": 0.2818461060523987,
2383
+ "learning_rate": 0.0001856738951844759,
2384
+ "loss": 0.9156,
2385
+ "step": 336
2386
+ },
2387
+ {
2388
+ "epoch": 0.71,
2389
+ "grad_norm": 1.2286068201065063,
2390
+ "learning_rate": 0.00018558804790685588,
2391
+ "loss": 2.6577,
2392
+ "step": 337
2393
+ },
2394
+ {
2395
+ "epoch": 0.71,
2396
+ "grad_norm": 0.7086435556411743,
2397
+ "learning_rate": 0.00018550196415209914,
2398
+ "loss": 0.8172,
2399
+ "step": 338
2400
+ },
2401
+ {
2402
+ "epoch": 0.71,
2403
+ "grad_norm": 1.0317937135696411,
2404
+ "learning_rate": 0.00018541564415805258,
2405
+ "loss": 1.3381,
2406
+ "step": 339
2407
+ },
2408
+ {
2409
+ "epoch": 0.72,
2410
+ "grad_norm": 0.693418562412262,
2411
+ "learning_rate": 0.00018532908816321558,
2412
+ "loss": 1.1259,
2413
+ "step": 340
2414
+ },
2415
+ {
2416
+ "epoch": 0.72,
2417
+ "grad_norm": 1.25714910030365,
2418
+ "learning_rate": 0.00018524229640673974,
2419
+ "loss": 0.7892,
2420
+ "step": 341
2421
+ },
2422
+ {
2423
+ "epoch": 0.72,
2424
+ "grad_norm": 0.6042699813842773,
2425
+ "learning_rate": 0.00018515526912842796,
2426
+ "loss": 0.8982,
2427
+ "step": 342
2428
+ },
2429
+ {
2430
+ "epoch": 0.72,
2431
+ "grad_norm": 0.3453720211982727,
2432
+ "learning_rate": 0.00018506800656873398,
2433
+ "loss": 0.9424,
2434
+ "step": 343
2435
+ },
2436
+ {
2437
+ "epoch": 0.72,
2438
+ "grad_norm": 0.7436335682868958,
2439
+ "learning_rate": 0.0001849805089687615,
2440
+ "loss": 0.7121,
2441
+ "step": 344
2442
+ },
2443
+ {
2444
+ "epoch": 0.73,
2445
+ "grad_norm": 0.8308970928192139,
2446
+ "learning_rate": 0.00018489277657026375,
2447
+ "loss": 1.1099,
2448
+ "step": 345
2449
+ },
2450
+ {
2451
+ "epoch": 0.73,
2452
+ "grad_norm": 0.6892271637916565,
2453
+ "learning_rate": 0.0001848048096156426,
2454
+ "loss": 0.6814,
2455
+ "step": 346
2456
+ },
2457
+ {
2458
+ "epoch": 0.73,
2459
+ "grad_norm": 0.30851200222969055,
2460
+ "learning_rate": 0.00018471660834794805,
2461
+ "loss": 0.283,
2462
+ "step": 347
2463
+ },
2464
+ {
2465
+ "epoch": 0.73,
2466
+ "grad_norm": 0.2706887722015381,
2467
+ "learning_rate": 0.00018462817301087748,
2468
+ "loss": 0.6258,
2469
+ "step": 348
2470
+ },
2471
+ {
2472
+ "epoch": 0.73,
2473
+ "grad_norm": 0.9876924157142639,
2474
+ "learning_rate": 0.00018453950384877504,
2475
+ "loss": 0.6983,
2476
+ "step": 349
2477
+ },
2478
+ {
2479
+ "epoch": 0.74,
2480
+ "grad_norm": 0.3037252128124237,
2481
+ "learning_rate": 0.0001844506011066308,
2482
+ "loss": 0.9854,
2483
+ "step": 350
2484
+ },
2485
+ {
2486
+ "epoch": 0.74,
2487
+ "grad_norm": 1.0091379880905151,
2488
+ "learning_rate": 0.00018436146503008035,
2489
+ "loss": 0.9871,
2490
+ "step": 351
2491
+ },
2492
+ {
2493
+ "epoch": 0.74,
2494
+ "grad_norm": 0.5219744443893433,
2495
+ "learning_rate": 0.0001842720958654039,
2496
+ "loss": 0.3771,
2497
+ "step": 352
2498
+ },
2499
+ {
2500
+ "epoch": 0.74,
2501
+ "grad_norm": 0.49409008026123047,
2502
+ "learning_rate": 0.00018418249385952575,
2503
+ "loss": 1.0838,
2504
+ "step": 353
2505
+ },
2506
+ {
2507
+ "epoch": 0.75,
2508
+ "grad_norm": 0.29014095664024353,
2509
+ "learning_rate": 0.00018409265926001343,
2510
+ "loss": 0.9922,
2511
+ "step": 354
2512
+ },
2513
+ {
2514
+ "epoch": 0.75,
2515
+ "grad_norm": 0.3307441771030426,
2516
+ "learning_rate": 0.00018400259231507717,
2517
+ "loss": 1.0458,
2518
+ "step": 355
2519
+ },
2520
+ {
2521
+ "epoch": 0.75,
2522
+ "grad_norm": 0.3356322646141052,
2523
+ "learning_rate": 0.00018391229327356916,
2524
+ "loss": 0.9891,
2525
+ "step": 356
2526
+ },
2527
+ {
2528
+ "epoch": 0.75,
2529
+ "grad_norm": 0.3707556426525116,
2530
+ "learning_rate": 0.00018382176238498286,
2531
+ "loss": 0.9578,
2532
+ "step": 357
2533
+ },
2534
+ {
2535
+ "epoch": 0.75,
2536
+ "eval_loss": 0.8826732635498047,
2537
+ "eval_runtime": 61.8974,
2538
+ "eval_samples_per_second": 1.616,
2539
+ "eval_steps_per_second": 1.616,
2540
+ "step": 357
2541
+ },
2542
+ {
2543
+ "epoch": 0.75,
2544
+ "grad_norm": 0.7507327198982239,
2545
+ "learning_rate": 0.00018373099989945236,
2546
+ "loss": 0.8916,
2547
+ "step": 358
2548
+ },
2549
+ {
2550
+ "epoch": 0.76,
2551
+ "grad_norm": 0.3686985373497009,
2552
+ "learning_rate": 0.00018364000606775155,
2553
+ "loss": 0.9855,
2554
+ "step": 359
2555
+ },
2556
+ {
2557
+ "epoch": 0.76,
2558
+ "grad_norm": 0.34240958094596863,
2559
+ "learning_rate": 0.00018354878114129367,
2560
+ "loss": 1.0874,
2561
+ "step": 360
2562
+ },
2563
+ {
2564
+ "epoch": 0.76,
2565
+ "grad_norm": 0.2911188304424286,
2566
+ "learning_rate": 0.00018345732537213027,
2567
+ "loss": 1.2217,
2568
+ "step": 361
2569
+ },
2570
+ {
2571
+ "epoch": 0.76,
2572
+ "grad_norm": 0.5415646433830261,
2573
+ "learning_rate": 0.0001833656390129509,
2574
+ "loss": 0.6675,
2575
+ "step": 362
2576
+ },
2577
+ {
2578
+ "epoch": 0.76,
2579
+ "grad_norm": 0.36682239174842834,
2580
+ "learning_rate": 0.00018327372231708212,
2581
+ "loss": 0.8702,
2582
+ "step": 363
2583
+ },
2584
+ {
2585
+ "epoch": 0.77,
2586
+ "grad_norm": 0.5462591648101807,
2587
+ "learning_rate": 0.0001831815755384869,
2588
+ "loss": 0.9005,
2589
+ "step": 364
2590
+ },
2591
+ {
2592
+ "epoch": 0.77,
2593
+ "grad_norm": 0.5059930682182312,
2594
+ "learning_rate": 0.00018308919893176396,
2595
+ "loss": 0.8994,
2596
+ "step": 365
2597
+ },
2598
+ {
2599
+ "epoch": 0.77,
2600
+ "grad_norm": 0.6344266533851624,
2601
+ "learning_rate": 0.00018299659275214706,
2602
+ "loss": 1.1571,
2603
+ "step": 366
2604
+ },
2605
+ {
2606
+ "epoch": 0.77,
2607
+ "grad_norm": 0.2552272081375122,
2608
+ "learning_rate": 0.00018290375725550417,
2609
+ "loss": 1.2492,
2610
+ "step": 367
2611
+ },
2612
+ {
2613
+ "epoch": 0.77,
2614
+ "grad_norm": 0.5543289184570312,
2615
+ "learning_rate": 0.00018281069269833692,
2616
+ "loss": 1.0141,
2617
+ "step": 368
2618
+ },
2619
+ {
2620
+ "epoch": 0.78,
2621
+ "grad_norm": 1.3686586618423462,
2622
+ "learning_rate": 0.0001827173993377798,
2623
+ "loss": 0.8264,
2624
+ "step": 369
2625
+ },
2626
+ {
2627
+ "epoch": 0.78,
2628
+ "grad_norm": 0.5549390912055969,
2629
+ "learning_rate": 0.0001826238774315995,
2630
+ "loss": 1.0753,
2631
+ "step": 370
2632
+ },
2633
+ {
2634
+ "epoch": 0.78,
2635
+ "grad_norm": 0.8563418388366699,
2636
+ "learning_rate": 0.00018253012723819416,
2637
+ "loss": 0.4458,
2638
+ "step": 371
2639
+ },
2640
+ {
2641
+ "epoch": 0.78,
2642
+ "grad_norm": 0.4292491376399994,
2643
+ "learning_rate": 0.00018243614901659264,
2644
+ "loss": 1.1994,
2645
+ "step": 372
2646
+ },
2647
+ {
2648
+ "epoch": 0.79,
2649
+ "grad_norm": 0.37186571955680847,
2650
+ "learning_rate": 0.00018234194302645394,
2651
+ "loss": 0.9811,
2652
+ "step": 373
2653
+ },
2654
+ {
2655
+ "epoch": 0.79,
2656
+ "grad_norm": 0.6655788421630859,
2657
+ "learning_rate": 0.00018224750952806624,
2658
+ "loss": 0.5048,
2659
+ "step": 374
2660
+ },
2661
+ {
2662
+ "epoch": 0.79,
2663
+ "grad_norm": 0.7731723785400391,
2664
+ "learning_rate": 0.00018215284878234642,
2665
+ "loss": 0.9481,
2666
+ "step": 375
2667
+ },
2668
+ {
2669
+ "epoch": 0.79,
2670
+ "grad_norm": 0.36243554949760437,
2671
+ "learning_rate": 0.00018205796105083915,
2672
+ "loss": 1.0048,
2673
+ "step": 376
2674
+ },
2675
+ {
2676
+ "epoch": 0.79,
2677
+ "grad_norm": 1.08484947681427,
2678
+ "learning_rate": 0.00018196284659571639,
2679
+ "loss": 1.0245,
2680
+ "step": 377
2681
+ },
2682
+ {
2683
+ "epoch": 0.8,
2684
+ "grad_norm": 1.128653883934021,
2685
+ "learning_rate": 0.00018186750567977637,
2686
+ "loss": 0.9997,
2687
+ "step": 378
2688
+ },
2689
+ {
2690
+ "epoch": 0.8,
2691
+ "grad_norm": 0.6685619950294495,
2692
+ "learning_rate": 0.00018177193856644316,
2693
+ "loss": 1.3555,
2694
+ "step": 379
2695
+ },
2696
+ {
2697
+ "epoch": 0.8,
2698
+ "grad_norm": 0.30426543951034546,
2699
+ "learning_rate": 0.00018167614551976567,
2700
+ "loss": 1.1209,
2701
+ "step": 380
2702
+ },
2703
+ {
2704
+ "epoch": 0.8,
2705
+ "grad_norm": 0.6189528107643127,
2706
+ "learning_rate": 0.00018158012680441723,
2707
+ "loss": 1.0321,
2708
+ "step": 381
2709
+ },
2710
+ {
2711
+ "epoch": 0.8,
2712
+ "grad_norm": 0.6775807738304138,
2713
+ "learning_rate": 0.00018148388268569453,
2714
+ "loss": 0.7826,
2715
+ "step": 382
2716
+ },
2717
+ {
2718
+ "epoch": 0.81,
2719
+ "grad_norm": 0.4594517946243286,
2720
+ "learning_rate": 0.00018138741342951705,
2721
+ "loss": 0.6422,
2722
+ "step": 383
2723
+ },
2724
+ {
2725
+ "epoch": 0.81,
2726
+ "grad_norm": 0.537011444568634,
2727
+ "learning_rate": 0.00018129071930242648,
2728
+ "loss": 0.9219,
2729
+ "step": 384
2730
+ },
2731
+ {
2732
+ "epoch": 0.81,
2733
+ "grad_norm": 0.43772855401039124,
2734
+ "learning_rate": 0.00018119380057158568,
2735
+ "loss": 1.1737,
2736
+ "step": 385
2737
+ },
2738
+ {
2739
+ "epoch": 0.81,
2740
+ "grad_norm": 0.7221130132675171,
2741
+ "learning_rate": 0.00018109665750477806,
2742
+ "loss": 0.8636,
2743
+ "step": 386
2744
+ },
2745
+ {
2746
+ "epoch": 0.81,
2747
+ "grad_norm": 0.3437989354133606,
2748
+ "learning_rate": 0.00018099929037040694,
2749
+ "loss": 0.9238,
2750
+ "step": 387
2751
+ },
2752
+ {
2753
+ "epoch": 0.82,
2754
+ "grad_norm": 0.47244492173194885,
2755
+ "learning_rate": 0.00018090169943749476,
2756
+ "loss": 0.7715,
2757
+ "step": 388
2758
+ },
2759
+ {
2760
+ "epoch": 0.82,
2761
+ "grad_norm": 0.7109631299972534,
2762
+ "learning_rate": 0.0001808038849756822,
2763
+ "loss": 0.4109,
2764
+ "step": 389
2765
+ },
2766
+ {
2767
+ "epoch": 0.82,
2768
+ "grad_norm": 0.27005669474601746,
2769
+ "learning_rate": 0.00018070584725522762,
2770
+ "loss": 0.7158,
2771
+ "step": 390
2772
+ },
2773
+ {
2774
+ "epoch": 0.82,
2775
+ "grad_norm": 0.4006590247154236,
2776
+ "learning_rate": 0.00018060758654700622,
2777
+ "loss": 1.0167,
2778
+ "step": 391
2779
+ },
2780
+ {
2781
+ "epoch": 0.83,
2782
+ "grad_norm": 0.5627204179763794,
2783
+ "learning_rate": 0.00018050910312250931,
2784
+ "loss": 0.8679,
2785
+ "step": 392
2786
+ },
2787
+ {
2788
+ "epoch": 0.83,
2789
+ "grad_norm": 0.5019241571426392,
2790
+ "learning_rate": 0.00018041039725384352,
2791
+ "loss": 0.9163,
2792
+ "step": 393
2793
+ },
2794
+ {
2795
+ "epoch": 0.83,
2796
+ "grad_norm": 1.00431227684021,
2797
+ "learning_rate": 0.00018031146921373018,
2798
+ "loss": 0.676,
2799
+ "step": 394
2800
+ },
2801
+ {
2802
+ "epoch": 0.83,
2803
+ "grad_norm": 0.7062071561813354,
2804
+ "learning_rate": 0.0001802123192755044,
2805
+ "loss": 1.2407,
2806
+ "step": 395
2807
+ },
2808
+ {
2809
+ "epoch": 0.83,
2810
+ "grad_norm": 1.6554285287857056,
2811
+ "learning_rate": 0.00018011294771311435,
2812
+ "loss": 1.1187,
2813
+ "step": 396
2814
+ },
2815
+ {
2816
+ "epoch": 0.84,
2817
+ "grad_norm": 1.08072829246521,
2818
+ "learning_rate": 0.00018001335480112064,
2819
+ "loss": 0.4878,
2820
+ "step": 397
2821
+ },
2822
+ {
2823
+ "epoch": 0.84,
2824
+ "grad_norm": 0.3923906981945038,
2825
+ "learning_rate": 0.00017991354081469538,
2826
+ "loss": 0.7836,
2827
+ "step": 398
2828
+ },
2829
+ {
2830
+ "epoch": 0.84,
2831
+ "grad_norm": 0.20446747541427612,
2832
+ "learning_rate": 0.0001798135060296216,
2833
+ "loss": 0.4597,
2834
+ "step": 399
2835
+ },
2836
+ {
2837
+ "epoch": 0.84,
2838
+ "grad_norm": 0.5178759098052979,
2839
+ "learning_rate": 0.00017971325072229226,
2840
+ "loss": 1.7021,
2841
+ "step": 400
2842
+ },
2843
+ {
2844
+ "epoch": 0.84,
2845
+ "grad_norm": 0.5159180164337158,
2846
+ "learning_rate": 0.0001796127751697097,
2847
+ "loss": 0.6037,
2848
+ "step": 401
2849
+ },
2850
+ {
2851
+ "epoch": 0.85,
2852
+ "grad_norm": 0.9448319673538208,
2853
+ "learning_rate": 0.0001795120796494848,
2854
+ "loss": 0.8965,
2855
+ "step": 402
2856
+ },
2857
+ {
2858
+ "epoch": 0.85,
2859
+ "grad_norm": 1.0035223960876465,
2860
+ "learning_rate": 0.00017941116443983613,
2861
+ "loss": 0.9786,
2862
+ "step": 403
2863
+ },
2864
+ {
2865
+ "epoch": 0.85,
2866
+ "grad_norm": 0.26040011644363403,
2867
+ "learning_rate": 0.00017931002981958933,
2868
+ "loss": 0.8624,
2869
+ "step": 404
2870
+ },
2871
+ {
2872
+ "epoch": 0.85,
2873
+ "grad_norm": 0.518144965171814,
2874
+ "learning_rate": 0.00017920867606817625,
2875
+ "loss": 1.0095,
2876
+ "step": 405
2877
+ },
2878
+ {
2879
+ "epoch": 0.85,
2880
+ "grad_norm": 0.5256940722465515,
2881
+ "learning_rate": 0.00017910710346563416,
2882
+ "loss": 0.7392,
2883
+ "step": 406
2884
+ },
2885
+ {
2886
+ "epoch": 0.86,
2887
+ "grad_norm": 0.8347258567810059,
2888
+ "learning_rate": 0.000179005312292605,
2889
+ "loss": 0.8081,
2890
+ "step": 407
2891
+ },
2892
+ {
2893
+ "epoch": 0.86,
2894
+ "grad_norm": 0.8221095204353333,
2895
+ "learning_rate": 0.00017890330283033468,
2896
+ "loss": 1.1406,
2897
+ "step": 408
2898
+ },
2899
+ {
2900
+ "epoch": 0.86,
2901
+ "grad_norm": 0.8048923015594482,
2902
+ "learning_rate": 0.00017880107536067218,
2903
+ "loss": 1.4362,
2904
+ "step": 409
2905
+ },
2906
+ {
2907
+ "epoch": 0.86,
2908
+ "grad_norm": 1.9037342071533203,
2909
+ "learning_rate": 0.0001786986301660689,
2910
+ "loss": 1.2935,
2911
+ "step": 410
2912
+ },
2913
+ {
2914
+ "epoch": 0.87,
2915
+ "grad_norm": 0.5521582961082458,
2916
+ "learning_rate": 0.00017859596752957768,
2917
+ "loss": 1.0742,
2918
+ "step": 411
2919
+ },
2920
+ {
2921
+ "epoch": 0.87,
2922
+ "grad_norm": 1.052284598350525,
2923
+ "learning_rate": 0.00017849308773485226,
2924
+ "loss": 0.7661,
2925
+ "step": 412
2926
+ },
2927
+ {
2928
+ "epoch": 0.87,
2929
+ "grad_norm": 0.43000859022140503,
2930
+ "learning_rate": 0.00017838999106614632,
2931
+ "loss": 0.812,
2932
+ "step": 413
2933
+ },
2934
+ {
2935
+ "epoch": 0.87,
2936
+ "grad_norm": 0.7804751396179199,
2937
+ "learning_rate": 0.00017828667780831278,
2938
+ "loss": 0.7995,
2939
+ "step": 414
2940
+ },
2941
+ {
2942
+ "epoch": 0.87,
2943
+ "grad_norm": 0.5827552080154419,
2944
+ "learning_rate": 0.000178183148246803,
2945
+ "loss": 0.6489,
2946
+ "step": 415
2947
+ },
2948
+ {
2949
+ "epoch": 0.88,
2950
+ "grad_norm": 1.3453142642974854,
2951
+ "learning_rate": 0.00017807940266766593,
2952
+ "loss": 0.7154,
2953
+ "step": 416
2954
+ },
2955
+ {
2956
+ "epoch": 0.88,
2957
+ "grad_norm": 0.24924832582473755,
2958
+ "learning_rate": 0.00017797544135754744,
2959
+ "loss": 0.8061,
2960
+ "step": 417
2961
+ },
2962
+ {
2963
+ "epoch": 0.88,
2964
+ "grad_norm": 0.4459979236125946,
2965
+ "learning_rate": 0.0001778712646036894,
2966
+ "loss": 1.1167,
2967
+ "step": 418
2968
+ },
2969
+ {
2970
+ "epoch": 0.88,
2971
+ "grad_norm": 0.6095878481864929,
2972
+ "learning_rate": 0.000177766872693929,
2973
+ "loss": 0.8344,
2974
+ "step": 419
2975
+ },
2976
+ {
2977
+ "epoch": 0.88,
2978
+ "grad_norm": 0.43662723898887634,
2979
+ "learning_rate": 0.00017766226591669785,
2980
+ "loss": 1.0373,
2981
+ "step": 420
2982
+ },
2983
+ {
2984
+ "epoch": 0.89,
2985
+ "grad_norm": 0.8759774565696716,
2986
+ "learning_rate": 0.00017755744456102122,
2987
+ "loss": 1.0988,
2988
+ "step": 421
2989
+ },
2990
+ {
2991
+ "epoch": 0.89,
2992
+ "grad_norm": 1.1800742149353027,
2993
+ "learning_rate": 0.00017745240891651735,
2994
+ "loss": 0.7385,
2995
+ "step": 422
2996
+ },
2997
+ {
2998
+ "epoch": 0.89,
2999
+ "grad_norm": 0.5820197463035583,
3000
+ "learning_rate": 0.0001773471592733964,
3001
+ "loss": 0.9363,
3002
+ "step": 423
3003
+ },
3004
+ {
3005
+ "epoch": 0.89,
3006
+ "grad_norm": 0.6128491759300232,
3007
+ "learning_rate": 0.00017724169592245995,
3008
+ "loss": 0.7762,
3009
+ "step": 424
3010
+ },
3011
+ {
3012
+ "epoch": 0.89,
3013
+ "grad_norm": 0.5693449378013611,
3014
+ "learning_rate": 0.0001771360191551,
3015
+ "loss": 0.7526,
3016
+ "step": 425
3017
+ },
3018
+ {
3019
+ "epoch": 0.9,
3020
+ "grad_norm": 0.7725418210029602,
3021
+ "learning_rate": 0.00017703012926329815,
3022
+ "loss": 0.7019,
3023
+ "step": 426
3024
+ },
3025
+ {
3026
+ "epoch": 0.9,
3027
+ "grad_norm": 0.5068923234939575,
3028
+ "learning_rate": 0.0001769240265396249,
3029
+ "loss": 0.8308,
3030
+ "step": 427
3031
+ },
3032
+ {
3033
+ "epoch": 0.9,
3034
+ "grad_norm": 0.34859699010849,
3035
+ "learning_rate": 0.0001768177112772388,
3036
+ "loss": 0.9593,
3037
+ "step": 428
3038
+ },
3039
+ {
3040
+ "epoch": 0.9,
3041
+ "grad_norm": 0.34673023223876953,
3042
+ "learning_rate": 0.00017671118376988573,
3043
+ "loss": 1.0334,
3044
+ "step": 429
3045
+ },
3046
+ {
3047
+ "epoch": 0.91,
3048
+ "grad_norm": 0.5354735851287842,
3049
+ "learning_rate": 0.0001766044443118978,
3050
+ "loss": 1.2355,
3051
+ "step": 430
3052
+ },
3053
+ {
3054
+ "epoch": 0.91,
3055
+ "grad_norm": 1.2567592859268188,
3056
+ "learning_rate": 0.0001764974931981929,
3057
+ "loss": 0.8935,
3058
+ "step": 431
3059
+ },
3060
+ {
3061
+ "epoch": 0.91,
3062
+ "grad_norm": 0.4151657521724701,
3063
+ "learning_rate": 0.00017639033072427366,
3064
+ "loss": 1.1042,
3065
+ "step": 432
3066
+ },
3067
+ {
3068
+ "epoch": 0.91,
3069
+ "grad_norm": 0.4307219386100769,
3070
+ "learning_rate": 0.00017628295718622665,
3071
+ "loss": 1.2273,
3072
+ "step": 433
3073
+ },
3074
+ {
3075
+ "epoch": 0.91,
3076
+ "grad_norm": 0.6330164074897766,
3077
+ "learning_rate": 0.0001761753728807217,
3078
+ "loss": 1.2027,
3079
+ "step": 434
3080
+ },
3081
+ {
3082
+ "epoch": 0.92,
3083
+ "grad_norm": 0.47434625029563904,
3084
+ "learning_rate": 0.00017606757810501088,
3085
+ "loss": 0.9242,
3086
+ "step": 435
3087
+ },
3088
+ {
3089
+ "epoch": 0.92,
3090
+ "grad_norm": 1.0463887453079224,
3091
+ "learning_rate": 0.00017595957315692782,
3092
+ "loss": 1.151,
3093
+ "step": 436
3094
+ },
3095
+ {
3096
+ "epoch": 0.92,
3097
+ "grad_norm": 0.7210713028907776,
3098
+ "learning_rate": 0.00017585135833488692,
3099
+ "loss": 0.8223,
3100
+ "step": 437
3101
+ },
3102
+ {
3103
+ "epoch": 0.92,
3104
+ "grad_norm": 0.5121049284934998,
3105
+ "learning_rate": 0.00017574293393788235,
3106
+ "loss": 0.6994,
3107
+ "step": 438
3108
+ },
3109
+ {
3110
+ "epoch": 0.92,
3111
+ "grad_norm": 0.8933761119842529,
3112
+ "learning_rate": 0.00017563430026548734,
3113
+ "loss": 0.846,
3114
+ "step": 439
3115
+ },
3116
+ {
3117
+ "epoch": 0.93,
3118
+ "grad_norm": 0.5270050764083862,
3119
+ "learning_rate": 0.0001755254576178535,
3120
+ "loss": 0.7119,
3121
+ "step": 440
3122
+ },
3123
+ {
3124
+ "epoch": 0.93,
3125
+ "grad_norm": 0.37028369307518005,
3126
+ "learning_rate": 0.0001754164062957096,
3127
+ "loss": 0.8623,
3128
+ "step": 441
3129
+ },
3130
+ {
3131
+ "epoch": 0.93,
3132
+ "grad_norm": 0.6245588660240173,
3133
+ "learning_rate": 0.00017530714660036112,
3134
+ "loss": 1.0409,
3135
+ "step": 442
3136
+ },
3137
+ {
3138
+ "epoch": 0.93,
3139
+ "grad_norm": 0.878105640411377,
3140
+ "learning_rate": 0.0001751976788336892,
3141
+ "loss": 0.6867,
3142
+ "step": 443
3143
+ },
3144
+ {
3145
+ "epoch": 0.93,
3146
+ "grad_norm": 0.3765283226966858,
3147
+ "learning_rate": 0.00017508800329814995,
3148
+ "loss": 1.2251,
3149
+ "step": 444
3150
+ },
3151
+ {
3152
+ "epoch": 0.94,
3153
+ "grad_norm": 0.4110933840274811,
3154
+ "learning_rate": 0.00017497812029677344,
3155
+ "loss": 0.8676,
3156
+ "step": 445
3157
+ },
3158
+ {
3159
+ "epoch": 0.94,
3160
+ "grad_norm": 1.3986817598342896,
3161
+ "learning_rate": 0.000174868030133163,
3162
+ "loss": 1.6298,
3163
+ "step": 446
3164
+ },
3165
+ {
3166
+ "epoch": 0.94,
3167
+ "grad_norm": 0.4310443103313446,
3168
+ "learning_rate": 0.0001747577331114945,
3169
+ "loss": 0.7328,
3170
+ "step": 447
3171
+ },
3172
+ {
3173
+ "epoch": 0.94,
3174
+ "grad_norm": 1.5922423601150513,
3175
+ "learning_rate": 0.00017464722953651504,
3176
+ "loss": 0.6629,
3177
+ "step": 448
3178
+ },
3179
+ {
3180
+ "epoch": 0.95,
3181
+ "grad_norm": 2.27004075050354,
3182
+ "learning_rate": 0.00017453651971354264,
3183
+ "loss": 1.4748,
3184
+ "step": 449
3185
+ },
3186
+ {
3187
+ "epoch": 0.95,
3188
+ "grad_norm": 0.31181880831718445,
3189
+ "learning_rate": 0.00017442560394846516,
3190
+ "loss": 1.0477,
3191
+ "step": 450
3192
+ },
3193
+ {
3194
+ "epoch": 0.95,
3195
+ "grad_norm": 1.1180263757705688,
3196
+ "learning_rate": 0.00017431448254773944,
3197
+ "loss": 0.9225,
3198
+ "step": 451
3199
+ },
3200
+ {
3201
+ "epoch": 0.95,
3202
+ "grad_norm": 0.7490403056144714,
3203
+ "learning_rate": 0.00017420315581839044,
3204
+ "loss": 0.7847,
3205
+ "step": 452
3206
+ },
3207
+ {
3208
+ "epoch": 0.95,
3209
+ "grad_norm": 1.138551115989685,
3210
+ "learning_rate": 0.0001740916240680105,
3211
+ "loss": 1.2782,
3212
+ "step": 453
3213
+ },
3214
+ {
3215
+ "epoch": 0.96,
3216
+ "grad_norm": 0.9375423789024353,
3217
+ "learning_rate": 0.0001739798876047584,
3218
+ "loss": 0.8316,
3219
+ "step": 454
3220
+ },
3221
+ {
3222
+ "epoch": 0.96,
3223
+ "grad_norm": 1.0941681861877441,
3224
+ "learning_rate": 0.0001738679467373586,
3225
+ "loss": 0.9702,
3226
+ "step": 455
3227
+ },
3228
+ {
3229
+ "epoch": 0.96,
3230
+ "grad_norm": 0.2845444083213806,
3231
+ "learning_rate": 0.00017375580177510016,
3232
+ "loss": 0.8563,
3233
+ "step": 456
3234
+ },
3235
+ {
3236
+ "epoch": 0.96,
3237
+ "grad_norm": 0.7341310381889343,
3238
+ "learning_rate": 0.0001736434530278362,
3239
+ "loss": 0.7102,
3240
+ "step": 457
3241
+ },
3242
+ {
3243
+ "epoch": 0.96,
3244
+ "grad_norm": 0.886854350566864,
3245
+ "learning_rate": 0.0001735309008059829,
3246
+ "loss": 1.4872,
3247
+ "step": 458
3248
+ },
3249
+ {
3250
+ "epoch": 0.97,
3251
+ "grad_norm": 0.44623202085494995,
3252
+ "learning_rate": 0.00017341814542051845,
3253
+ "loss": 0.8142,
3254
+ "step": 459
3255
+ },
3256
+ {
3257
+ "epoch": 0.97,
3258
+ "grad_norm": 0.8813387155532837,
3259
+ "learning_rate": 0.00017330518718298264,
3260
+ "loss": 1.0869,
3261
+ "step": 460
3262
+ },
3263
+ {
3264
+ "epoch": 0.97,
3265
+ "grad_norm": 0.8468006253242493,
3266
+ "learning_rate": 0.0001731920264054755,
3267
+ "loss": 1.2859,
3268
+ "step": 461
3269
+ },
3270
+ {
3271
+ "epoch": 0.97,
3272
+ "grad_norm": 0.6797575354576111,
3273
+ "learning_rate": 0.00017307866340065685,
3274
+ "loss": 1.0288,
3275
+ "step": 462
3276
+ },
3277
+ {
3278
+ "epoch": 0.97,
3279
+ "grad_norm": 0.25991517305374146,
3280
+ "learning_rate": 0.00017296509848174508,
3281
+ "loss": 0.7996,
3282
+ "step": 463
3283
+ },
3284
+ {
3285
+ "epoch": 0.98,
3286
+ "grad_norm": 0.557244598865509,
3287
+ "learning_rate": 0.00017285133196251663,
3288
+ "loss": 0.6877,
3289
+ "step": 464
3290
+ },
3291
+ {
3292
+ "epoch": 0.98,
3293
+ "grad_norm": 0.49986258149147034,
3294
+ "learning_rate": 0.00017273736415730488,
3295
+ "loss": 0.8285,
3296
+ "step": 465
3297
+ },
3298
+ {
3299
+ "epoch": 0.98,
3300
+ "grad_norm": 0.5839115977287292,
3301
+ "learning_rate": 0.0001726231953809993,
3302
+ "loss": 0.8617,
3303
+ "step": 466
3304
+ },
3305
+ {
3306
+ "epoch": 0.98,
3307
+ "grad_norm": 1.1967806816101074,
3308
+ "learning_rate": 0.0001725088259490448,
3309
+ "loss": 0.719,
3310
+ "step": 467
3311
+ },
3312
+ {
3313
+ "epoch": 0.99,
3314
+ "grad_norm": 0.7830839157104492,
3315
+ "learning_rate": 0.00017239425617744048,
3316
+ "loss": 0.623,
3317
+ "step": 468
3318
+ },
3319
+ {
3320
+ "epoch": 0.99,
3321
+ "grad_norm": 2.0930089950561523,
3322
+ "learning_rate": 0.00017227948638273916,
3323
+ "loss": 1.1768,
3324
+ "step": 469
3325
+ },
3326
+ {
3327
+ "epoch": 0.99,
3328
+ "grad_norm": 0.562641441822052,
3329
+ "learning_rate": 0.0001721645168820462,
3330
+ "loss": 0.5235,
3331
+ "step": 470
3332
+ },
3333
+ {
3334
+ "epoch": 0.99,
3335
+ "grad_norm": 0.5656068325042725,
3336
+ "learning_rate": 0.00017204934799301883,
3337
+ "loss": 0.9211,
3338
+ "step": 471
3339
+ },
3340
+ {
3341
+ "epoch": 0.99,
3342
+ "grad_norm": 0.934866726398468,
3343
+ "learning_rate": 0.0001719339800338651,
3344
+ "loss": 0.7897,
3345
+ "step": 472
3346
+ },
3347
+ {
3348
+ "epoch": 1.0,
3349
+ "grad_norm": 0.30100950598716736,
3350
+ "learning_rate": 0.00017181841332334318,
3351
+ "loss": 1.0436,
3352
+ "step": 473
3353
+ },
3354
+ {
3355
+ "epoch": 1.0,
3356
+ "grad_norm": 0.5494408011436462,
3357
+ "learning_rate": 0.00017170264818076026,
3358
+ "loss": 0.6412,
3359
+ "step": 474
3360
+ },
3361
+ {
3362
+ "epoch": 1.0,
3363
+ "grad_norm": 0.6607212424278259,
3364
+ "learning_rate": 0.00017158668492597186,
3365
+ "loss": 1.3501,
3366
+ "step": 475
3367
+ }
3368
+ ],
3369
+ "logging_steps": 1,
3370
+ "max_steps": 1900,
3371
+ "num_input_tokens_seen": 0,
3372
+ "num_train_epochs": 4,
3373
+ "save_steps": 475,
3374
+ "total_flos": 3.489377344094208e+16,
3375
+ "train_batch_size": 1,
3376
+ "trial_name": null,
3377
+ "trial_params": null
3378
+ }
checkpoint-475/training_args.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:a86f7a599d796e7b48de991f534e6b4a66f2f12ac3aac1d99bcffb3fefbdb76d
3
+ size 5816
checkpoint-475/vocab.json ADDED
The diff for this file is too large to render. See raw diff
 
checkpoint-950/README.md ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: peft
3
+ base_model: Qwen/Qwen1.5-MoE-A2.7B
4
+ ---
5
+
6
+ # Model Card for Model ID
7
+
8
+ <!-- Provide a quick summary of what the model is/does. -->
9
+
10
+
11
+
12
+ ## Model Details
13
+
14
+ ### Model Description
15
+
16
+ <!-- Provide a longer summary of what this model is. -->
17
+
18
+
19
+
20
+ - **Developed by:** [More Information Needed]
21
+ - **Funded by [optional]:** [More Information Needed]
22
+ - **Shared by [optional]:** [More Information Needed]
23
+ - **Model type:** [More Information Needed]
24
+ - **Language(s) (NLP):** [More Information Needed]
25
+ - **License:** [More Information Needed]
26
+ - **Finetuned from model [optional]:** [More Information Needed]
27
+
28
+ ### Model Sources [optional]
29
+
30
+ <!-- Provide the basic links for the model. -->
31
+
32
+ - **Repository:** [More Information Needed]
33
+ - **Paper [optional]:** [More Information Needed]
34
+ - **Demo [optional]:** [More Information Needed]
35
+
36
+ ## Uses
37
+
38
+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
39
+
40
+ ### Direct Use
41
+
42
+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
43
+
44
+ [More Information Needed]
45
+
46
+ ### Downstream Use [optional]
47
+
48
+ <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
49
+
50
+ [More Information Needed]
51
+
52
+ ### Out-of-Scope Use
53
+
54
+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
55
+
56
+ [More Information Needed]
57
+
58
+ ## Bias, Risks, and Limitations
59
+
60
+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
61
+
62
+ [More Information Needed]
63
+
64
+ ### Recommendations
65
+
66
+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
67
+
68
+ Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
69
+
70
+ ## How to Get Started with the Model
71
+
72
+ Use the code below to get started with the model.
73
+
74
+ [More Information Needed]
75
+
76
+ ## Training Details
77
+
78
+ ### Training Data
79
+
80
+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
81
+
82
+ [More Information Needed]
83
+
84
+ ### Training Procedure
85
+
86
+ <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
87
+
88
+ #### Preprocessing [optional]
89
+
90
+ [More Information Needed]
91
+
92
+
93
+ #### Training Hyperparameters
94
+
95
+ - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
96
+
97
+ #### Speeds, Sizes, Times [optional]
98
+
99
+ <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
100
+
101
+ [More Information Needed]
102
+
103
+ ## Evaluation
104
+
105
+ <!-- This section describes the evaluation protocols and provides the results. -->
106
+
107
+ ### Testing Data, Factors & Metrics
108
+
109
+ #### Testing Data
110
+
111
+ <!-- This should link to a Dataset Card if possible. -->
112
+
113
+ [More Information Needed]
114
+
115
+ #### Factors
116
+
117
+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
118
+
119
+ [More Information Needed]
120
+
121
+ #### Metrics
122
+
123
+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
124
+
125
+ [More Information Needed]
126
+
127
+ ### Results
128
+
129
+ [More Information Needed]
130
+
131
+ #### Summary
132
+
133
+
134
+
135
+ ## Model Examination [optional]
136
+
137
+ <!-- Relevant interpretability work for the model goes here -->
138
+
139
+ [More Information Needed]
140
+
141
+ ## Environmental Impact
142
+
143
+ <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
144
+
145
+ Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
146
+
147
+ - **Hardware Type:** [More Information Needed]
148
+ - **Hours used:** [More Information Needed]
149
+ - **Cloud Provider:** [More Information Needed]
150
+ - **Compute Region:** [More Information Needed]
151
+ - **Carbon Emitted:** [More Information Needed]
152
+
153
+ ## Technical Specifications [optional]
154
+
155
+ ### Model Architecture and Objective
156
+
157
+ [More Information Needed]
158
+
159
+ ### Compute Infrastructure
160
+
161
+ [More Information Needed]
162
+
163
+ #### Hardware
164
+
165
+ [More Information Needed]
166
+
167
+ #### Software
168
+
169
+ [More Information Needed]
170
+
171
+ ## Citation [optional]
172
+
173
+ <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
174
+
175
+ **BibTeX:**
176
+
177
+ [More Information Needed]
178
+
179
+ **APA:**
180
+
181
+ [More Information Needed]
182
+
183
+ ## Glossary [optional]
184
+
185
+ <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
186
+
187
+ [More Information Needed]
188
+
189
+ ## More Information [optional]
190
+
191
+ [More Information Needed]
192
+
193
+ ## Model Card Authors [optional]
194
+
195
+ [More Information Needed]
196
+
197
+ ## Model Card Contact
198
+
199
+ [More Information Needed]
200
+ ### Framework versions
201
+
202
+ - PEFT 0.10.0
checkpoint-950/adapter_config.json ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "alpha_pattern": {},
3
+ "auto_mapping": null,
4
+ "base_model_name_or_path": "Qwen/Qwen1.5-MoE-A2.7B",
5
+ "bias": "none",
6
+ "fan_in_fan_out": null,
7
+ "inference_mode": true,
8
+ "init_lora_weights": true,
9
+ "layer_replication": null,
10
+ "layers_pattern": null,
11
+ "layers_to_transform": null,
12
+ "loftq_config": {},
13
+ "lora_alpha": 16,
14
+ "lora_dropout": 0.05,
15
+ "megatron_config": null,
16
+ "megatron_core": "megatron.core",
17
+ "modules_to_save": null,
18
+ "peft_type": "LORA",
19
+ "r": 32,
20
+ "rank_pattern": {},
21
+ "revision": null,
22
+ "target_modules": [
23
+ "gate",
24
+ "k_proj",
25
+ "up_proj",
26
+ "v_proj",
27
+ "o_proj",
28
+ "gate_proj",
29
+ "shared_expert_gate",
30
+ "q_proj",
31
+ "down_proj"
32
+ ],
33
+ "task_type": "CAUSAL_LM",
34
+ "use_dora": false,
35
+ "use_rslora": false
36
+ }
checkpoint-950/adapter_model.safetensors ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f6a3d8e63ba16b25387f5e6697a0b2be8dd8c783f48fa02f444b3b2e75c8066f
3
+ size 2046221176
checkpoint-950/added_tokens.json ADDED
@@ -0,0 +1,5 @@
 
 
 
 
 
 
1
+ {
2
+ "<|endoftext|>": 151643,
3
+ "<|im_end|>": 151645,
4
+ "<|im_start|>": 151644
5
+ }