Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +88 -0
- added_tokens.json +12 -0
- config.json +158 -0
- generation_config.json +10 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.json +3 -0
- tokenizer_config.json +112 -0
- vocab.json +0 -0
.gitattributes
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@@ -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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license: apache-2.0
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language:
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- vi
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base_model: [microsoft/phi-4-mini-instruct]
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tags:
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- finetuned
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- lora
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- quantized
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- vietnamese
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- vmlu
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datasets:
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- 5CD-AI/Vietnamese-nampdn-ai-tiny-webtext-gg-translated
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metrics:
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- VMLU
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---
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# Phi-4-mini-Vietnamese-Instruct
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## Model Description
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- **Base Model:** `microsoft/phi-4-mini-instruct`
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- **Finetuning Technique:** Low-Rank Adaptation (LoRA)
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- **Quantization:** 4-bit NF4 using `bitsandbytes`
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- **Purpose:** To create a powerful yet lightweight model capable of understanding and generating high-quality Vietnamese text.
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The LoRA weights were merged into the base model, and the resulting model was quantized to optimize for performance and reduce memory footprint, making it suitable for deployment on consumer-grade hardware.
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## How to Use
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As the LoRA weights have been merged, you can use this model directly with the `transformers` library without needing the `peft` library for inference.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "your-username/your-repo-name"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.bfloat16, # Use bfloat16 for faster inference
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trust_remote_code=True
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)
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# Create a prompt using the chat template
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# This is the recommended way for instruction-tuned models
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messages = [
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{"role": "user", "content": "Hãy viết một đoạn văn ngắn giải thích về Lượng tử hóa trong AI."},
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]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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# Generate text
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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eos_token_id=tokenizer.eos_token_id,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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# Print the generated text, extracting only the assistant's response
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print(response.split("<|assistant|>")[1].strip())
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```
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## Finetuning Details
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The model was fine-tuned on the [5CD-AI/Vietnamese-nampdn-ai-tiny-webtext-gg-translated](https://huggingface.co/datasets/5CD-AI/Vietnamese-nampdn-ai-tiny-webtext-gg-translated) dataset.
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- LoRA Rank: 16
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- LoRA Alpha: 32
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- Training Epochs: 1
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- Number of Sample: 500000
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## Evaluation results
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The model's performance was evaluated on the Vietnamese Machine Learning Understanding (VMLU) benchmark.
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| Model | Social Science | Stem | Humanities | Others | Avg |
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| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: |
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| Phi-4 mini Vietnamese | 40.85 | 48 | 42.06 | 43.31 | 42.84 |
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added_tokens.json
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{
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"<|/tool_call|>": 200026,
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"<|/tool|>": 200024,
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"<|assistant|>": 200019,
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"<|end|>": 200020,
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"<|system|>": 200022,
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"<|tag|>": 200028,
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"<|tool_call|>": 200025,
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"<|tool_response|>": 200027,
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"<|tool|>": 200023,
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"<|user|>": 200021
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}
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config.json
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{
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"architectures": [
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"Phi3ForCausalLM"
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],
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"attention_bias": false,
|
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "microsoft/Phi-4-mini-instruct--configuration_phi3.Phi3Config",
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9 |
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"AutoModelForCausalLM": "microsoft/Phi-4-mini-instruct--modeling_phi3.Phi3ForCausalLM",
|
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"AutoTokenizer": "microsoft/Phi-4-mini-instruct--Xenova/gpt-4o"
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},
|
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"bos_token_id": 199999,
|
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"embd_pdrop": 0.0,
|
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"eos_token_id": 199999,
|
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"full_attn_mod": 1,
|
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"hidden_act": "silu",
|
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"hidden_size": 3072,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 8192,
|
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"interpolate_factor": 1,
|
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"lm_head_bias": false,
|
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
|
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"model_type": "phi3",
|
25 |
+
"num_attention_heads": 24,
|
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"num_hidden_layers": 32,
|
27 |
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"num_key_value_heads": 8,
|
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"original_max_position_embeddings": 4096,
|
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"pad_token_id": 199999,
|
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"partial_rotary_factor": 0.75,
|
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+
"quantization_config": {
|
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+
"_load_in_4bit": true,
|
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+
"_load_in_8bit": false,
|
34 |
+
"bnb_4bit_compute_dtype": "bfloat16",
|
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"bnb_4bit_quant_storage": "uint8",
|
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"bnb_4bit_quant_type": "nf4",
|
37 |
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"bnb_4bit_use_double_quant": true,
|
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"llm_int8_enable_fp32_cpu_offload": false,
|
39 |
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"llm_int8_has_fp16_weight": false,
|
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"llm_int8_skip_modules": null,
|
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"llm_int8_threshold": 6.0,
|
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"load_in_4bit": true,
|
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"load_in_8bit": false,
|
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"quant_method": "bitsandbytes"
|
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},
|
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"resid_pdrop": 0.0,
|
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"rms_norm_eps": 1e-05,
|
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"rope_scaling": {
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"long_factor": [
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}
|
generation_config.json
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{
|
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"_from_model_config": true,
|
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"bos_token_id": 199999,
|
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"eos_token_id": [
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|
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|
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"transformers_version": "4.51.3"
|
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}
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merges.txt
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:953386f337ec332de9a2ec01e32ad4ac256ce5bd0b320ee02ee9487e9f755346
|
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size 2891584577
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special_tokens_map.json
ADDED
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{
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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},
|
23 |
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|
24 |
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|
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|
26 |
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|
27 |
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|
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|
29 |
+
}
|
30 |
+
}
|
tokenizer.json
ADDED
@@ -0,0 +1,3 @@
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|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:382cc235b56c725945e149cc25f191da667c836655efd0857b004320e90e91ea
|
3 |
+
size 15524095
|
tokenizer_config.json
ADDED
@@ -0,0 +1,112 @@
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|
1 |
+
{
|
2 |
+
"add_bos_token": false,
|
3 |
+
"add_eos_token": false,
|
4 |
+
"add_prefix_space": false,
|
5 |
+
"added_tokens_decoder": {
|
6 |
+
"199999": {
|
7 |
+
"content": "<|endoftext|>",
|
8 |
+
"lstrip": false,
|
9 |
+
"normalized": false,
|
10 |
+
"rstrip": false,
|
11 |
+
"single_word": false,
|
12 |
+
"special": true
|
13 |
+
},
|
14 |
+
"200018": {
|
15 |
+
"content": "<|endofprompt|>",
|
16 |
+
"lstrip": false,
|
17 |
+
"normalized": false,
|
18 |
+
"rstrip": false,
|
19 |
+
"single_word": false,
|
20 |
+
"special": true
|
21 |
+
},
|
22 |
+
"200019": {
|
23 |
+
"content": "<|assistant|>",
|
24 |
+
"lstrip": false,
|
25 |
+
"normalized": false,
|
26 |
+
"rstrip": true,
|
27 |
+
"single_word": false,
|
28 |
+
"special": true
|
29 |
+
},
|
30 |
+
"200020": {
|
31 |
+
"content": "<|end|>",
|
32 |
+
"lstrip": false,
|
33 |
+
"normalized": false,
|
34 |
+
"rstrip": true,
|
35 |
+
"single_word": false,
|
36 |
+
"special": true
|
37 |
+
},
|
38 |
+
"200021": {
|
39 |
+
"content": "<|user|>",
|
40 |
+
"lstrip": false,
|
41 |
+
"normalized": false,
|
42 |
+
"rstrip": true,
|
43 |
+
"single_word": false,
|
44 |
+
"special": true
|
45 |
+
},
|
46 |
+
"200022": {
|
47 |
+
"content": "<|system|>",
|
48 |
+
"lstrip": false,
|
49 |
+
"normalized": false,
|
50 |
+
"rstrip": true,
|
51 |
+
"single_word": false,
|
52 |
+
"special": true
|
53 |
+
},
|
54 |
+
"200023": {
|
55 |
+
"content": "<|tool|>",
|
56 |
+
"lstrip": false,
|
57 |
+
"normalized": false,
|
58 |
+
"rstrip": true,
|
59 |
+
"single_word": false,
|
60 |
+
"special": false
|
61 |
+
},
|
62 |
+
"200024": {
|
63 |
+
"content": "<|/tool|>",
|
64 |
+
"lstrip": false,
|
65 |
+
"normalized": false,
|
66 |
+
"rstrip": true,
|
67 |
+
"single_word": false,
|
68 |
+
"special": false
|
69 |
+
},
|
70 |
+
"200025": {
|
71 |
+
"content": "<|tool_call|>",
|
72 |
+
"lstrip": false,
|
73 |
+
"normalized": false,
|
74 |
+
"rstrip": true,
|
75 |
+
"single_word": false,
|
76 |
+
"special": false
|
77 |
+
},
|
78 |
+
"200026": {
|
79 |
+
"content": "<|/tool_call|>",
|
80 |
+
"lstrip": false,
|
81 |
+
"normalized": false,
|
82 |
+
"rstrip": true,
|
83 |
+
"single_word": false,
|
84 |
+
"special": false
|
85 |
+
},
|
86 |
+
"200027": {
|
87 |
+
"content": "<|tool_response|>",
|
88 |
+
"lstrip": false,
|
89 |
+
"normalized": false,
|
90 |
+
"rstrip": true,
|
91 |
+
"single_word": false,
|
92 |
+
"special": false
|
93 |
+
},
|
94 |
+
"200028": {
|
95 |
+
"content": "<|tag|>",
|
96 |
+
"lstrip": false,
|
97 |
+
"normalized": false,
|
98 |
+
"rstrip": true,
|
99 |
+
"single_word": false,
|
100 |
+
"special": true
|
101 |
+
}
|
102 |
+
},
|
103 |
+
"bos_token": "<|endoftext|>",
|
104 |
+
"chat_template": "{% for message in messages %}{% if message['role'] == 'system' and 'tools' in message and message['tools'] is not none %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|tool|>' + message['tools'] + '<|/tool|>' + '<|end|>' }}{% else %}{{ '<|' + message['role'] + '|>' + message['content'] + '<|end|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>' }}{% else %}{{ eos_token }}{% endif %}",
|
105 |
+
"clean_up_tokenization_spaces": false,
|
106 |
+
"eos_token": "<|endoftext|>",
|
107 |
+
"extra_special_tokens": {},
|
108 |
+
"model_max_length": 131072,
|
109 |
+
"pad_token": "<|endoftext|>",
|
110 |
+
"tokenizer_class": "GPT2Tokenizer",
|
111 |
+
"unk_token": "<|endoftext|>"
|
112 |
+
}
|
vocab.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|