初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Canum-med-Qwen3-Reasoning
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- mteb/raw_medrxiv
+language:
+- en
+- zh
+base_model:
+- prithivMLmods/Qwen3-1.7B-ft-bf16
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- trl
+- text-generation-inference
+- medical
+- article
+- biology
+- med
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/FFOM9ye5qFOr6Jpef_yyb.png)
+
+# **Canum-med-Qwen3-Reasoning (Experimental)**
+
+> **Canum-med-Qwen3-Reasoning** is an **experimental medical reasoning and advisory model** fine-tuned on **Qwen/Qwen3-1.7B** using the **MTEB/raw\_medrxiv** dataset.
+> It is designed to support **clinical reasoning, biomedical understanding, and structured advisory outputs**, making it a useful tool for researchers, educators, and medical professionals in experimental workflows.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning-GGUF](https://huggingface.co/prithivMLmods/Canum-med-Qwen3-Reasoning-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Medical Reasoning Focus**
+   Fine-tuned on **MTEB/raw\_medrxiv**, enabling strong performance in **biomedical literature understanding**, diagnostic reasoning, and structured medical advisory tasks.
+
+2. **Clinical Knowledge Extraction**
+   Summarizes, interprets, and explains medical research papers, case studies, and treatment comparisons.
+
+3. **Step-by-Step Advisory**
+   Provides structured reasoning chains for **symptom analysis, medical explanations, and advisory workflows**.
+
+4. **Evidence-Aware Responses**
+   Optimized for scientific precision and evidence-driven output, suitable for **research assistance** and **medical tutoring**.
+
+5. **Structured Output Mastery**
+   Capable of producing results in **LaTeX**, **Markdown**, **JSON**, and **tabular formats**, supporting integration into research and healthcare informatics systems.
+
+6. **Optimized for Mid-Scale Deployment**
+   Balanced efficiency for **research clusters**, **academic labs**, and **edge deployments in healthcare AI prototypes**.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Canum-med-Qwen3-Reasoning"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Summarize the findings of a study on the effectiveness of mRNA vaccines for COVID-19."
+
+messages = [
+    {"role": "system", "content": "You are a medical reasoning assistant that explains biomedical studies and provides structured clinical insights."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* **Medical research summarization** and literature review
+* **Diagnostic reasoning assistance** for educational or research purposes
+* **Clinical advisory explanations** in structured step-by-step format
+* **Biomedical tutoring** for students and researchers
+* **Integration into experimental healthcare AI pipelines**
+
+## **Limitations**
+
+* ⚠️ **Not a replacement for medical professionals** – should not be used for direct clinical decision-making
+* Training limited to research text corpora – may not capture rare or real-world patient-specific contexts
+* Context length limits restrict multi-document medical record analysis
+* Optimized for reasoning and structure, not empathetic or conversational dialogue