初始化项目，由ModelHub XC社区提供模型
Model: Mix80/ClinicaQwen-MedQA Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-1.5B
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tags:
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- medical
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- text-generation
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- fine-tuned
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- qa
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metrics:
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- perplexity
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pipeline_tag: text-generation
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---
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# ClinicaQwen-MedQA (1.5B)
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**ClinicaQwen-MedQA** is a lightweight, hyper-specialized medical question-answering assistant. It is built upon the robust `Qwen/Qwen2.5-1.5B` architecture and has undergone a rigorous dual-stage training pipeline to eliminate hallucinations and maximize clinical accuracy while remaining deployable on low-VRAM edge hardware.
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## 🌟 Key Features
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- **Dual-Stage Optimized:** Trained sequentially for deep medical terminology comprehension and strict conversational QA alignment.
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- **Completion-Loss Masked:** Fine-tuned using masked training targets to eliminate prompt mirroring ("echo bugs").
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- **Ultra-Efficient:** Runs seamlessly in native `bfloat16` precision on consumer or serverless GPU instances (e.g., RunPod, vLLM).
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## 📊 Training & Evaluation Performance
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### Stage 1: Medical Knowledge Injection
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- **Dataset:** `gamino/wiki_medical_terms`
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- **Final Evaluation Loss:** 2.0314
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- **Perplexity:** 7.63
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### Stage 2: Conversational QA Alignment
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- **Dataset:** `medalpaca/medical_meadow_medical_flashcards`
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- **Final Evaluation Loss:** 0.9755
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- **Perplexity:** 2.65
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---
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## 🚀 How to Use (Python Example)
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You can load and query **ClinicaQwen-MedQA** directly using the Hugging Face `transformers` library. Ensure your inference script uses low temperatures for maximum medical accuracy.
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### Installation
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```bash
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pip install torch transformers acceleration safetensors
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```
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### Inference Code
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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 = "Mix80/ClinicaQwen-MedQA"
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print("Loading ClinicaQwen-MedQA...")
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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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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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def ask_clinica_qwen(question, max_new_tokens=150):
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# Construct the strict conversational template
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prompt = f"User: {question}\nBot:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_new_tokens,
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temperature=0.1, # Crucial: Low temp ensures strict factual adherence
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top_p=0.85,
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do_sample=True,
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repetition_penalty=1.3, # Prevents linguistic loops
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no_repeat_ngram_size=3,
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pad_token_id=tokenizer.eos_token_id
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)
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# Extract only the generated answer tokens, skipping the prompt prefix
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prompt_length = inputs.input_ids.shape[1]
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generated_tokens = outputs[0][prompt_length:]
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answer = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip()
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return answer
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# Test the model
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sample_query = "What are the key differences between a tension headache and a migraine?"
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response = ask_clinica_qwen(sample_query)
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print(f"\nQuestion: {sample_query}")
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print(f"ClinicaQwen: {response}")
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```
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---
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## ⚠️ Disclaimer
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*ClinicaQwen-MedQA is an AI research model developed for educational and experimental data extraction purposes. It is not a licensed medical tool and should not be used as a substitute for professional clinical diagnosis, advice, or treatment workflows.*
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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 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 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",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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