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Model: launch/MET-D-Qwen3-8B Source: Original Platform
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---
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license: apache-2.0
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library_name: transformers
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base_model: Qwen/Qwen3-8B
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pipeline_tag: text-generation
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tags:
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- moral reasoning
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- value reasoning
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- persona
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- chain-of-thought
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language:
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- en
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- es
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- hi
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- ko
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- ms
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- zh
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---
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# Model Card for MET-D-Qwen3-8B
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MET-D-Qwen3-8B is a multilingual moral reasoning model fine-tuned from [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). Given a moral dilemma, a character description, and a candidate action, it judges the action from that character's perspective and explains its judgment with an explicit chain-of-thought before answering. Moral dilemmas rarely have a single correct answer, which makes reasoning traces hard to verify. We address this by introducing a character perspective that yields a ground-truth answer, which is used for rejection-sampling the model's own reasoning traces, conditioned on a per-language, per-situation selection of theoretical grounds. Both the reasoning trace and the final answer are generated in whichever language the prompt is written in, so non-English speakers get legible reasoning in their own language.
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## Model Details
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- **Base model:** Qwen/Qwen3-8B
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- **Task:** for a given `(situation, character description, action)` triple, answer two questions from the character's perspective:
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1. Is the action acceptable? (`Yes` / `No` / `Ambiguous`)
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2. Would (not) doing it be emotionally/mentally discomforting? (`Yes` / `No`)
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- **Training data:** self-generated reasoning traces, rejection-sampled against the ground truth per character perspective, covering six languages (English, Spanish, Hindi, Korean, Malay, Chinese)
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- **Language coverage:** this checkpoint is trained on **all 6 languages combined**. Single-language variants are also available (see [Model Variants](#model-variants) below).
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- **License:** Apache 2.0 (inherited from the base model)
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## How to Get Started
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```python
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from transformers import AutoTokenizer
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from vllm import LLM, SamplingParams
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model_id = "launch/MET-D-Qwen3-8B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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llm = LLM(model=model_id, max_model_len=16384)
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situation = "..." # the moral dilemma narrative
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character = "..." # the character description
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action = "..." # the candidate action to judge
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prompt = f"""I will provide you a moral dilemma and present you with a character description and questions.
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The answer for the first question should be Yes, No, or Ambiguous, and the answer for the second question should be Yes or No.
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You should strictly adhere to the output format, since it would be automatically parsed.
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Make sure to output both the "Reason:" and "Answer:" for all 2 questions.
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Situation:
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{situation}
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Character Description:
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{character}
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Question:
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1. From A's perspective, would the following action be deemed acceptable? "{action}"
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2. If the previous answer is yes, would A find doing the action mentally or emotionally discomforting? Conversely, if the previous answer is no, would A find not doing the action discomforting?
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Your Answer:
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1. Reason: {{Reason}} Answer: {{Yes/No/Ambiguous}}
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2. Reason: {{Reason}} Answer: {{Yes/No}}
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"""
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chat_prompt = tokenizer.apply_chat_template(
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[{"role": "user", "content": prompt}],
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tokenize=False,
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add_generation_prompt=True,
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)
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sampling_params = SamplingParams(temperature=0.0, max_tokens=2048)
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outputs = llm.generate(chat_prompt, sampling_params)
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print(outputs[0].outputs[0].text)
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```
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## Model Variants
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This checkpoint is part of the [MET collection](https://huggingface.co/collections/launch/met), which includes the same task across base models and language subsets:
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| Repo | Base model | Language(s) |
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|---|---|---|
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| `launch/MET-D-Qwen3-4B` | Qwen3-4B | all 6 (mixed) |
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| `launch/MET-D-Qwen3-4B-en-only` | Qwen3-4B | English only |
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| `launch/MET-D-Qwen3-4B-es-only` | Qwen3-4B | Spanish only |
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| `launch/MET-D-Qwen3-4B-hi-only` | Qwen3-4B | Hindi only |
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| `launch/MET-D-Qwen3-4B-ko-only` | Qwen3-4B | Korean only |
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| `launch/MET-D-Qwen3-4B-ms-only` | Qwen3-4B | Malay only |
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| `launch/MET-D-Qwen3-4B-zh-only` | Qwen3-4B | Chinese only |
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| `launch/MET-D-Qwen3-8B` | Qwen3-8B | all 6 (mixed) |
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| `launch/MET-D-Qwen3-8B-en-only` | Qwen3-8B | English only |
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| `launch/MET-D-Gemma3-4B` | Gemma-3-4B-it | all 6 (mixed) |
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| `launch/MET-D-Gemma3-4B-en-only` | Gemma-3-4B-it | English only |
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## Citation
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If you use this, please cite:
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```bibtex
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@article{lee2026met,
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title={MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning},
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author={Lee, Ayoung and Kwon, Ryan and Zhang, Yunxiang and Liu, Yuxuan and Railton, Peter and Wang, Lu},
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journal={arXiv preprint arXiv:2607.11736},
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year={2026}
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}
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```
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"architectures": [
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
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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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||||
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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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|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
241
tokenizer_config.json
Normal file
241
tokenizer_config.json
Normal file
@@ -0,0 +1,241 @@
|
||||
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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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||||
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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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||||
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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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||||
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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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||||
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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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||||
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||||
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||||
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|
||||
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|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\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 {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- 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 {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
83
trainer_state.json
Normal file
83
trainer_state.json
Normal file
@@ -0,0 +1,83 @@
|
||||
{
|
||||
"best_global_step": null,
|
||||
"best_metric": null,
|
||||
"best_model_checkpoint": null,
|
||||
"epoch": 0.9896907216494846,
|
||||
"eval_steps": 500,
|
||||
"global_step": 72,
|
||||
"is_hyper_param_search": false,
|
||||
"is_local_process_zero": true,
|
||||
"is_world_process_zero": true,
|
||||
"log_history": [
|
||||
{
|
||||
"epoch": 0.13745704467353953,
|
||||
"grad_norm": 2.4307763198718733,
|
||||
"learning_rate": 4.996988640512931e-06,
|
||||
"loss": 0.388,
|
||||
"step": 10
|
||||
},
|
||||
{
|
||||
"epoch": 0.27491408934707906,
|
||||
"grad_norm": 1.0906284673832232,
|
||||
"learning_rate": 4.644321525000681e-06,
|
||||
"loss": 0.2703,
|
||||
"step": 20
|
||||
},
|
||||
{
|
||||
"epoch": 0.41237113402061853,
|
||||
"grad_norm": 1.2566825629380436,
|
||||
"learning_rate": 3.785256860483054e-06,
|
||||
"loss": 0.2409,
|
||||
"step": 30
|
||||
},
|
||||
{
|
||||
"epoch": 0.5498281786941581,
|
||||
"grad_norm": 1.028796931927551,
|
||||
"learning_rate": 2.6226691858185454e-06,
|
||||
"loss": 0.2431,
|
||||
"step": 40
|
||||
},
|
||||
{
|
||||
"epoch": 0.6872852233676976,
|
||||
"grad_norm": 1.1617643684029584,
|
||||
"learning_rate": 1.4311122664242955e-06,
|
||||
"loss": 0.2394,
|
||||
"step": 50
|
||||
},
|
||||
{
|
||||
"epoch": 0.8247422680412371,
|
||||
"grad_norm": 0.9873573640523043,
|
||||
"learning_rate": 4.91981171298388e-07,
|
||||
"loss": 0.234,
|
||||
"step": 60
|
||||
},
|
||||
{
|
||||
"epoch": 0.9621993127147767,
|
||||
"grad_norm": 0.8050782530173689,
|
||||
"learning_rate": 2.7058725088047466e-08,
|
||||
"loss": 0.2185,
|
||||
"step": 70
|
||||
}
|
||||
],
|
||||
"logging_steps": 10,
|
||||
"max_steps": 72,
|
||||
"num_input_tokens_seen": 0,
|
||||
"num_train_epochs": 1,
|
||||
"save_steps": 200,
|
||||
"stateful_callbacks": {
|
||||
"TrainerControl": {
|
||||
"args": {
|
||||
"should_epoch_stop": false,
|
||||
"should_evaluate": false,
|
||||
"should_log": false,
|
||||
"should_save": true,
|
||||
"should_training_stop": true
|
||||
},
|
||||
"attributes": {}
|
||||
}
|
||||
},
|
||||
"total_flos": 15087599255552.0,
|
||||
"train_batch_size": 1,
|
||||
"trial_name": null,
|
||||
"trial_params": null
|
||||
}
|
||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:6947ac7b32cc871cdb420f6edc2da3e936782af3a0e92792e67e8a30617163d8
|
||||
size 7953
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user