初始化项目，由ModelHub XC社区提供模型

Model: BEncoderRT/medical_inference
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+datasets:
+- FreedomIntelligence/medical-o1-reasoning-SFT
+language:
+- zh
+base_model:
+- unsloth/DeepSeek-R1-Distill-Llama-8B
+pipeline_tag: text-generation
+tags:
+- unsloth
+- deepseek
+- rola
+- sft
+---
+
+# DeepSeek-R1 Medical Reasoning Model
+
+This repository contains a **fine-tuned medical reasoning model** based on  
+[DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-8B)  
+and trained on the [medical-o1-reasoning-SFT](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT) dataset.
+
+⚠️ **The uploaded file (`unsloth.Q8_0.gguf`) contains quantized weights** for efficient inference.
+
+---
+
+## 🔍 Model Overview
+
+- **Base Model**: unsloth/DeepSeek-R1-Distill-Llama-8B  
+- **Training Method**: SFT (Supervised Fine-Tuning)  
+- **Domain**: Medical reasoning and clinical knowledge  
+- **Language**: English  
+- **Quantization**: Q8_0 (gguf format for efficient inference)  
+
+---
+
+## 📚 Training Data
+
+The model was fine-tuned on:
+
+- **Dataset**: `FreedomIntelligence/medical-o1-reasoning-SFT`  
+- **Language**: English  
+- **Task**: Medical reasoning, clinical question-answering  
+
+---
+
+## 🚀 Usage Example
+
+> **Note:** The model is stored in `.gguf` format (quantized). You can load it using `unsloth` library.
+
+```python
+from unsloth import FastLanguageModel
+import torch
+
+# Load the quantized GGUF model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    "./unsloth.Q8_0.gguf",
+    max_seq_length=2048,
+    load_in_8bit=True,  # optional depending on quantization
+)
+
+FastLanguageModel.for_inference(model)
+
+def generate(model, prompt, max_new_tokens=200):
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+        )
+
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Example prompt
+prompt = """### Instruction:
+"一个患有急性阑尾炎的病人已经发病5天，腹痛稍有减轻但仍然发热，在体检时发现右下腹有压痛的包块，此时应如何处理？"
+### Response:
+"""
+
+print(generate(model, prompt))
+
+
+<think>
+这个病人已经有五天的急性阑尾炎了，腹痛虽然有所缓解，但他还是发烧，这让我有点担心。再看看他的体检结果，右下腹有压痛的包块，这就让我更有理由担心。嗯，既然有包块，而且病人还是发烧，应该是急性阑尾炎的急性症状。
+
+想到这些，我觉得应该马上处理一下。最好的办法就是立即做腹部超声检查，看看包块的具体情况。超声检查可以帮助我们了解包块的位置、大小、形态以及周围组织的状态，这些信息对治疗非常重要。
+
+哦，对了，包块如果是液体的，可能就不需要手术了。我们可以通过超声检查来确定这一点。这样就能避免不必要的手术创伤。
+
+但是如果包块是固体的，特别是当它对周围组织有压迫作用时，手术就变得必要了。我们需要根据包块的具体情况来决定是否手术。
+
+另外，包块的形态和位置也会影响手术的选择。我们需要根据这些因素来制定具体的手术方案。
+
+嗯，总结一下，首先必须做一个腹部超声检查，确定包块的类型和具体情况。只有在确认包块是固体并且对周围组织有压迫作用的时候，才需要考虑手术。
+
+好的，这个思路看起来很合理。现在就要把这些想法转化为具体的处理步骤。
+</think>
+在这种情况下，建议首先进行腹部超声检查，以确定包块的类型和具体情况。根据检查结果：
+
+1. **如果包块是液体性包块**：通常不需要手术。可以通过药物治疗缓解症状，随后观察病情缓解情况。
+
+2. **如果包块是固体性包块并且对周围组织有压迫作用**：这时手术就变得必要了。通常需要进行包块切除术，以解除压迫，缓解症状。
+
+请尽快安排进行腹部超声检查，并根据检查结果制定最合适的手术方案。<｜end▁of▁sentence｜>

config.json

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+{
+    "model_type": "llama"
+}

unsloth.Q8_0.gguf

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+version https://git-lfs.github.com/spec/v1
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+size 8540773152