初始化项目，由ModelHub XC社区提供模型

Model: prithivMLmods/Sculptor-Qwen3_Med-Reasoning
Source: Original Platform

README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+- UCSC-VLAA/MedReason
+base_model:
+- Qwen/Qwen3-1.7B
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- text-generation-inference
+- moe
+- medical
+- biology
+- trl
+---
+
+![Add a heading.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/wq6VfaFU623sx351YhRdQ.png)
+
+# Sculptor-Qwen3\_Med-Reasoning
+
+> **Sculptor-Qwen3\_Med-Reasoning** is a fine-tuned variant of the **Qwen3-4B** architecture, trained specifically on the **Med Reason Dataset** to maximize **accurate medical and clinical reasoning**. This model excels at structured diagnostic logic, symptom analysis, and treatment planning, while maintaining lightweight performance, making it ideal for healthcare, medical education, and clinical support applications.
+
+> [!note]
+[!GGUF] : https://huggingface.co/prithivMLmods/Sculptor-Qwen3_Med-Reasoning-Q4_K_M-GGUF
+ 
+## Key Features
+
+1. **Precision Medical Reasoning with Med Reason Dataset**
+   Tailored for clinical reasoning, medical question answering, and evidence-based analysis, powered by the specialized Med Reason fine-tuning.
+
+2. **Lightweight Clinical Code Understanding**
+   Capable of interpreting and generating medical-related code (e.g., for health data analysis in Python or R), optimized for concise, logic-oriented scripts.
+
+3. **Structured Output Formatting**
+   Produces well-organized responses in Markdown, JSON, LaTeX, and tabular formats suitable for electronic health records, research documentation, and structured reporting.
+
+4. **Instruction-Following Accuracy**
+   Tuned for consistent multi-step instruction adherence in clinical cases and decision-making workflows, enhancing reliability for educational and medical use.
+
+5. **Multilingual Medical Capabilities**
+   Supports clinical reasoning and documentation in over 20 languages, enabling accessibility for global healthcare professionals.
+
+6. **Efficient 4B Architecture**
+   Based on Qwen3-4B, offering a balanced tradeoff between inference speed and domain-specific accuracy—suitable for deployment on mid-tier GPUs or cloud-based systems.
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Sculptor-Qwen3_Med-Reasoning"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "A 45-year-old male presents with chest pain and shortness of breath. List possible diagnoses and explain the reasoning."
+
+messages = [
+    {"role": "system", "content": "You are a clinical reasoning assistant trained on the Med Reason Dataset."},
+    {"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
+
+* Clinical reasoning and diagnosis support
+* Medical question answering and tutoring
+* Structured documentation and case analysis
+* JSON/Markdown/tabular medical summaries
+* Education tools for healthcare professionals
+* Multilingual medical documentation and Q\&A
+
+## Limitations
+
+* Not designed for open-domain creative generation
+* Limited context length compared to larger LLMs
+* Sensitive to ambiguous or poorly formatted inputs
+* May produce errors in complex or adversarial medical prompts
+
+## References
+
+1. [Qwen2.5 Technical Report](https://arxiv.org/pdf/2412.15115)
+2. [YaRN: Context Window Extension for LLMs](https://arxiv.org/pdf/2309.00071)