Model save
Browse files- .gitattributes +1 -0
- README.md +58 -0
- added_tokens.json +24 -0
- all_results.json +9 -0
- config.json +29 -0
- generation_config.json +14 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +25 -0
- tokenizer.json +3 -0
- tokenizer_config.json +208 -0
- train_results.json +9 -0
- trainer_state.json +704 -0
- training_args.bin +3 -0
- vocab.json +0 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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library_name: transformers
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model_name: Qwen2.5-0.5B-Open-R1-Distill
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tags:
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- generated_from_trainer
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- trl
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- sft
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licence: license
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---
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# Model Card for Qwen2.5-0.5B-Open-R1-Distill
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This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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It has been trained using [TRL](https://github.com/huggingface/trl).
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## Quick start
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```python
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from transformers import pipeline
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question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
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generator = pipeline("text-generation", model="xdrshjr/Qwen2.5-0.5B-Open-R1-Distill", device="cuda")
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output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
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print(output["generated_text"])
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```
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## Training procedure
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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/xdrshjr/huggingface/runs/huebpabw)
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This model was trained with SFT.
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### Framework versions
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- TRL: 0.15.0.dev0
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- Transformers: 4.49.0.dev0
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- Pytorch: 2.5.1+cu121
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- Datasets: 3.2.0
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- Tokenizers: 0.21.0
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## Citations
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Cite TRL as:
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```bibtex
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@misc{vonwerra2022trl,
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title = {{TRL: Transformer Reinforcement Learning}},
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author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
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year = 2020,
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journal = {GitHub repository},
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publisher = {GitHub},
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howpublished = {\url{https://github.com/huggingface/trl}}
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}
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```
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added_tokens.json
ADDED
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{
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"</tool_call>": 151658,
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"<|endoftext|>": 151643,
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"<|fim_middle|>": 151660,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_start|>": 151652
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}
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all_results.json
ADDED
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{
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"epoch": 0.9994447529150472,
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"total_flos": 1.8998720188121088e+17,
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"train_loss": 0.9762679852379693,
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"train_runtime": 18617.8565,
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"train_samples": 16610,
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"train_samples_per_second": 2.321,
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"train_steps_per_second": 0.024
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}
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config.json
ADDED
@@ -0,0 +1,29 @@
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{
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"_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct",
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"architectures": [
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"Qwen2ForCausalLM"
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 896,
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"initializer_range": 0.02,
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"intermediate_size": 4864,
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
|
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"num_attention_heads": 14,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
|
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.49.0.dev0",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.49.0.dev0"
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}
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merges.txt
ADDED
The diff for this file is too large to render.
See raw diff
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:23130c09a9a7e32350fdf95e9c25d240c419cfac4f301dbeb175faf965a57e9b
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size 1260367448
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special_tokens_map.json
ADDED
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{
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"additional_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
|
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"<|box_start|>",
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"<|box_end|>",
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"<|quad_start|>",
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"<|quad_end|>",
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"<|vision_start|>",
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"<|vision_end|>",
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"<|vision_pad|>",
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"<|image_pad|>",
|
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"<|video_pad|>"
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],
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"eos_token": {
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"content": "<|im_end|>",
|
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"lstrip": false,
|
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"normalized": false,
|
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"rstrip": false,
|
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"single_word": false
|
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},
|
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"pad_token": "<|im_end|>"
|
25 |
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}
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tokenizer.json
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
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size 11421896
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tokenizer_config.json
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},
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|
94 |
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95 |
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|
96 |
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97 |
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98 |
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99 |
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|
100 |
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|
101 |
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|
102 |
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|
103 |
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104 |
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|
105 |
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|
106 |
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|
107 |
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|
108 |
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109 |
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|
110 |
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|
111 |
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112 |
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|
113 |
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114 |
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|
115 |
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|
116 |
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117 |
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|
118 |
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|
119 |
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|
120 |
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|
121 |
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|
122 |
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|
123 |
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|
124 |
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|
125 |
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|
126 |
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|
127 |
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|
128 |
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|
129 |
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|
130 |
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|
131 |
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|
132 |
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133 |
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|
134 |
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135 |
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136 |
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137 |
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|
138 |
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139 |
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140 |
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141 |
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142 |
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143 |
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144 |
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145 |
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146 |
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147 |
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148 |
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149 |
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150 |
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152 |
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154 |
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155 |
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156 |
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162 |
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163 |
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164 |
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165 |
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|
166 |
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167 |
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168 |
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169 |
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170 |
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171 |
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172 |
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173 |
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|
174 |
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175 |
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176 |
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177 |
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178 |
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179 |
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180 |
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181 |
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182 |
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183 |
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|
184 |
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185 |
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186 |
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187 |
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188 |
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189 |
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190 |
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191 |
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192 |
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193 |
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|
194 |
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|
195 |
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|
196 |
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197 |
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198 |
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"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
199 |
+
"clean_up_tokenization_spaces": false,
|
200 |
+
"eos_token": "<|im_end|>",
|
201 |
+
"errors": "replace",
|
202 |
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"extra_special_tokens": {},
|
203 |
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"model_max_length": 131072,
|
204 |
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"pad_token": "<|im_end|>",
|
205 |
+
"split_special_tokens": false,
|
206 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
207 |
+
"unk_token": null
|
208 |
+
}
|
train_results.json
ADDED
@@ -0,0 +1,9 @@
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1 |
+
{
|
2 |
+
"epoch": 0.9994447529150472,
|
3 |
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"total_flos": 1.8998720188121088e+17,
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4 |
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"train_loss": 0.9762679852379693,
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5 |
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"train_runtime": 18617.8565,
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"train_samples": 16610,
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"train_samples_per_second": 2.321,
|
8 |
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"train_steps_per_second": 0.024
|
9 |
+
}
|
trainer_state.json
ADDED
@@ -0,0 +1,704 @@
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|
1 |
+
{
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2 |
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"best_metric": null,
|
3 |
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"best_model_checkpoint": null,
|
4 |
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|
5 |
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|
6 |
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|
7 |
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"is_hyper_param_search": false,
|
8 |
+
"is_local_process_zero": true,
|
9 |
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"is_world_process_zero": true,
|
10 |
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"log_history": [
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11 |
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{
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12 |
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13 |
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"learning_rate": 2.222222222222222e-06,
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"loss": 1.3609,
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"step": 5
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17 |
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},
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18 |
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{
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19 |
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20 |
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22 |
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23 |
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"step": 10
|
24 |
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},
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25 |
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{
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26 |
+
"epoch": 0.03331482509716824,
|
27 |
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"grad_norm": 2.8811240196228027,
|
28 |
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"learning_rate": 6.666666666666667e-06,
|
29 |
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"loss": 1.2626,
|
30 |
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"step": 15
|
31 |
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},
|
32 |
+
{
|
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