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Model: Rumiii/Qwen2.5-0.5B-Medical-ReasonMed370K
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+datasets:
+- lingshu-medical-mllm/ReasonMed
+base_model:
+- unsloth/Qwen2.5-0.5B-Instruct
+---
+## Info
+
+
+![AKmUU](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/zxUf9YsQPkICX5-n1BVjc.jpeg)
+
+![Demo Screenshot](https://cdn-uploads.huggingface.co/production/uploads/66e00ba55e4fd4bfead4a97c/ZWQpqF2613W9Ty9NBYUk1.png)
+
+# Qwen2.5-0.5B-Medical-ReasonMed370K
+
+A 0.5 billion parameter medical reasoning model fine-tuned on the complete ReasonMed 370K dataset. This model is built on top of Qwen2.5-0.5B-Instruct and trained to perform structured clinical reasoning, differential diagnosis, and evidence-based medical question answering.
+
+## Model Details
+
+- **Base Model**: unsloth/Qwen2.5-0.5B-Instruct
+- **Model Size**: 0.5B parameters
+- **Fine-tuning Method**: LoRA via Unsloth
+- **Training Dataset**: ReasonMed 370K (full dataset)
+- **Training Hardware**: NVIDIA Tesla T4 (Kaggle free tier)
+- **License**: Apache 2.0
+
+## Training Details
+
+The model was fine-tuned in two stages, each covering half of the ReasonMed dataset:
+
+**Stage 1**: Fine-tuned on the first 185,000 samples of ReasonMed using LoRA with the following configuration:
+- LoRA rank: 8
+- LoRA alpha: 16
+- Learning rate: 5e-5
+- Batch size: 2 with 16 gradient accumulation steps
+- Max sequence length: 4096
+- Epochs: 1
+- Optimizer: AdamW 8-bit
+
+**Stage 2**: Continued fine-tuning on the remaining 184,983 samples with identical configuration, completing one full pass over the entire 370K dataset.
+
+Both stages used `packing=False` to ensure every sample was processed individually without truncation.
+
+## Dataset
+
+This model was trained on [ReasonMed](https://huggingface.co/datasets/lingshu-medical-mllm/ReasonMed), the largest open-source medical reasoning dataset available, comprising 370,000 high-quality examples distilled from 1.75 million initial reasoning paths generated by multiple large language models.
+
+ReasonMed is built through a multi-agent verification and refinement pipeline that includes an Error Refiner to correct error-prone reasoning steps. Each example combines detailed chain-of-thought reasoning with a concise answer summary, covering a wide range of medical topics including clinical reasoning, differential diagnosis, pharmacology, and medical question answering.
+
+For more details on the dataset, refer to the official repository: https://github.com/alibaba-damo-academy/ReasonMed
+
+## What the Model Can Do
+
+After training on the full ReasonMed dataset, the model demonstrates the ability to:
+
+- Work through clinical presentations step by step
+- Generate differential diagnoses with reasoning for each option
+- Rule out unlikely diagnoses with justification
+- Provide structured final answers with clinical pearls
+- Reason through medical multiple choice questions with explanation
+
+## Demo
+
+The screenshot above shows the model running through a clinical scenario involving hypothyroidism, demonstrating its ability to identify key symptoms, interpret lab values, and produce a structured response with management guidance.
+
+## Limitations
+
+- This is a 0.5B parameter model and has a hard ceiling on reasoning depth and factual recall
+- Small models are prone to inconsistency across similar questions
+- The model may occasionally hallucinate clinical details
+- This model is intended for research and educational purposes only
+- It should not be used for real clinical decision making or as a substitute for a qualified medical professional
+
+## Usage
+```python
+from unsloth import FastLanguageModel
+import torch
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name     = "Rumiii/Qwen2.5-0.5B-Medical-ReasonMed370K",
+    max_seq_length = 4096,
+    load_in_4bit   = True,
+)
+FastLanguageModel.for_inference(model)
+
+messages = [
+    {"role": "user", "content": "Your medical question here"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize              = True,
+    add_generation_prompt = True,
+    return_tensors        = "pt"
+).to("cuda")
+
+outputs = model.generate(
+    input_ids            = inputs,
+    max_new_tokens       = 1024,
+    temperature          = 0.7,
+    do_sample            = True,
+    repetition_penalty   = 1.3,
+    no_repeat_ngram_size = 3,
+    top_p                = 0.9,
+    top_k                = 50,
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Citation
+
+If you use this model, please cite the ReasonMed dataset:
+```bibtex
+@misc{sun2025reasonmed370kmultiagentgenerated,
+      title={ReasonMed: A 370K Multi-Agent Generated Dataset for Advancing Medical Reasoning}, 
+      author={Yu Sun and Xingyu Qian and Weiwen Xu and Hao Zhang and Chenghao Xiao and Long Li and Yu Rong and Wenbing Huang and Qifeng Bai and Tingyang Xu},
+      year={2025},
+      eprint={2506.09513},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2506.09513}, 
+}
+```
+
+## Acknowledgements
+
+Training was conducted on Kaggle free tier infrastructure using Unsloth for efficient fine-tuning. The ReasonMed dataset was created by the team at Alibaba DAMO Academy and Tencent AI Lab.

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\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>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\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" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+    "architectures": [
+        "Qwen2ForCausalLM"
+    ],
+    "attention_dropout": 0.0,
+    "bos_token_id": null,
+    "torch_dtype": "float16",
+    "eos_token_id": 151645,
+    "hidden_act": "silu",
+    "hidden_size": 896,
+    "initializer_range": 0.02,
+    "intermediate_size": 4864,
+    "layer_types": [
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention",
+        "full_attention"
+    ],
+    "max_position_embeddings": 32768,
+    "max_window_layers": 21,
+    "model_type": "qwen2",
+    "num_attention_heads": 14,
+    "num_hidden_layers": 24,
+    "num_key_value_heads": 2,
+    "pad_token_id": 151665,
+    "rms_norm_eps": 1e-06,
+    "rope_parameters": {
+        "rope_theta": 1000000.0,
+        "rope_type": "default"
+    },
+    "sliding_window": null,
+    "tie_word_embeddings": true,
+    "unsloth_fixed": true,
+    "unsloth_version": "2026.3.4",
+    "use_cache": false,
+    "use_sliding_window": false,
+    "vocab_size": 151936
+}

model.safetensors

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+oid sha256:b190c378bb9e8f5ef27fb12de23e3f4591a68f4bc9600842ce3de0f4d69b2c62
+size 988097824

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "is_local": true,
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "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\") %}            {{- '<|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"
+}