初始化项目，由ModelHub XC社区提供模型
Model: BlueZeros/EHR-R1-1.7B Source: Original Platform
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README.md
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---
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license: cc-by-nc-4.0
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- medical
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- healthcare
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- ehr
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- reasoning
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- qwen
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language:
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- en
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base_model:
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- Qwen/Qwen3-1.7B
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---
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# EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis
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This repository contains the **EHR-R1-1.7B** model, part of the **EHR-R1** series, as presented in the paper [EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis](https://huggingface.co/papers/2510.25628).
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**EHR-R1** is a family of reasoning-enhanced Large Language Models (LLMs) specifically tailored for Electronic Health Record (EHR) analysis. It is developed based on **EHR-Ins**, a large-scale, comprehensive EHR reasoning instruction dataset, and is trained through a multi-stage paradigm including domain adaptation, reasoning enhancement, and reinforcement learning. This approach systematically acquires domain knowledge and diverse reasoning capabilities, enabling accurate and robust EHR analysis. The project also introduces **EHR-Bench**, a new benchmark curated from MIMIC-IV for comprehensive assessment across 42 distinct EHR tasks.
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* **Paper**: [https://huggingface.co/papers/2510.25628](https://huggingface.co/papers/2510.25628)
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* **GitHub Repository**: [https://github.com/MAGIC-AI4Med/EHR-R1](https://github.com/MAGIC-AI4Med/EHR-R1)
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<p align="center">
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<img src="https://github.com/MAGIC-AI4Med/EHR-R1/raw/main/assets/teaser.png" alt="EHR-R1 Teaser Image" width="800">
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</p>
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## 💡 Key Highlights
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* We open-source a large-scale instruction dataset [**EHR-Ins**](https://huggingface.co/datasets/BlueZeros/EHR-Ins-Reasoning), including 3.5M non-reasoning data and 300k reasoning data.
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* We open-source a comprehensive benchmark [**EHR-Bench**](https://huggingface.co/datasets/BlueZeros/EHR-Bench), which covers 42 distinct EHR analysis tasks.
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* We open-source EHR reasoning-enhanced LLMs **EHR-R1**, including [**EHR-R1-1.7B**](https://huggingface.co/BlueZeros/EHR-R1-1.7B), [**EHR-R1-8B**](https://huggingface.co/BlueZeros/EHR-R1-8B), and [**EHR-R1-72B**](https://huggingface.co/BlueZeros/EHR-R1-72B).
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* We open-source the "thinking-graph" pipeline, which can synthesize reasoning chains for EHR analysis tasks according to the relation of EHR entities.
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## ⚡ Directly Use
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### EHR Input Format
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For any EHR data, keep the EHR input with markdown format as below:
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* For the event with single record:
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```
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## Evant Name [Event Time (YYYY-MM-DD HH:MM:SS)]
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- ItemKey_1: ItemValue_1
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- ItemKey_2: ItemValue_2
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- ItemKey_3: ItemValue_3
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```
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* For the event with multiple records (like labevents):
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```
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## Evant Name [Event Time (YYYY-MM-DD HH:MM:SS)]
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| ItemKey_1 | ItemKey_2 | ItemKey_3 |
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| --------- | --------- | --------- |
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| ItemValue_1 | ItemValue_2 | ItemValue_3 |
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| ItemValue_1 | ItemValue_2 | ItemValue_3 |
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| ItemValue_1 | ItemValue_2 | ItemValue_3 |
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```
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### Models Inference with Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_name = "BlueZeros/EHR-R1-1.7B" # This specific EHR-R1-1.7B model
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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ehr_input = "{YOUR FOMATTED EHR INPUT}"
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instruction = "{YOUR TASK INSTRUCTION}"
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": ehr_input + "\n" + instruction}
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]
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# For EHR-R1-1.7B & EHR-R1-8B, control the reasoning mode by setting enable_thinking
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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enable_thinking=False,
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).to(model.device)
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# For EHR-R1-72B, you can manually add `<think>\n\n</think>` at the end of the model_inputs to close the reasoning modes.
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text += "<think>\n\n</think>"
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=2048,
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temperature=0.0
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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## 📖 Citation
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If you find our work helpful or inspiring, please feel free to cite it:
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```bib
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@article{liao2025ehrr1,
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title={{EHR-R1: A Reasoning-Enhanced Foundational Language Model for Electronic Health Record Analysis}},
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author={Liao, Yusheng and Wu, Chaoyi and Liu, Junwei and Jiang, Shuyang and Qiu, Pengcheng and Wang, Haowen and Yue, Yun and Zhen, Shuai and Wang, Jian and Fan, Qianrui and Gu, Jinjie and Zhang, Ya and Wang, Yanfeng and Wang, Yu and Xie, Weidi},
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journal={arXiv preprint arXiv:2510.25628},
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year={2025}
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}
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```
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config.json
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"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"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 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.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set content = message.content %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is defined and message.reasoning_content is not none %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in message.content %}\n {%- set content = message.content.split('</think>')[-1].lstrip('\\n') %}\n {%- set reasoning_content = message.content.split('</think>')[0].rstrip('\\n').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 {{- 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 {%- 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|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": 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 sha256:03f8db162f1fa25fc02f1a96db8a810da752f193076fb5ebe1a8c5a4bfd94eb9
|
||||||
|
size 8312
|
||||||
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