Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+  - llm
+  - mozihe
+  - agv
+  - defect-detection
+  - chinese
+  - english
+  - transformers
+  - ollama
+model_name: agv_llm
+base_model: meta-llama/Llama-3.1-8B
+library_name: transformers
+---
+
+# 📄 AGV-LLM
+
+> **Small enough to self-host, smart enough to 写巡检报告，分析缺陷数据**  
+> 8 B bilingual model fine-tuned for **tunnel-defect description** & **work-order drafting**.  
+> Works in both **Transformers** and **Ollama**.
+
+---
+
+## ✨ Highlights
+| Feature | Details |
+| ------- | ------- |
+| 🔧 **Domain-specific** | 56 K 巡检对话 / 工单指令数据 / 数据分析 |
+| 🧑‍🏫 **LoRA fine-tuned** | QLoRA-NF4, Rank 8, α = 16 |
+| 🈶 **Bilingual** | 中文 ↔ English |
+| ⚡ **Fast** | ~15 tok/s on RTX 4090 (fp16) |
+| 📦 **Drop-in** | `AutoModelForCausalLM` **or** `ollama pull mozihe/agv_llm` |
+
+---
+
+## 🛠️ Usage
+
+### Transformers
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch, textwrap
+
+tok = AutoTokenizer.from_pretrained("mozihe/agv_llm")
+model = AutoModelForCausalLM.from_pretrained(
+    "mozihe/agv_llm", torch_dtype=torch.float16, device_map="auto"
+)
+
+prompt = (
+  "请根据以下检测框信息，生成缺陷描述和整改建议：\\n"
+  "位置：x=12.3,y=1.2,z=7.8\\n种类：裂缝\\n置信度：0.87"
+)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=256, temperature=0.3)
+print(textwrap.fill(tok.decode(out[0], skip_special_tokens=True), 80))
+```
+
+### Ollama
+
+
+1. 构建本地模型并命名：
+
+```bash
+ollama create agv_llm -f Modelfile
+```
+
+2. 运行：
+
+```
+ollama run agv_llm
+```
+
+> 说明  
+> - `ADAPTER` 行既支持远程 Hugging Face 路径，也支持 `file://` 本地 .safetensors。  
+> - 更多 Modelfile 指令见 <https://github.com/ollama/ollama/blob/main/docs/modelfile.md>  
+---
+
+## 📚 Training Details
+| Item | Value |
+| ---- | ----- |
+| Base | Llama-3.1-8B |
+| Method | QLoRA (bitsandbytes NF4) |
+| Steps | 25 epochs |
+| LR / Scheduler | 1e-4 / cosine |
+| Context | 4 096 tokens |
+| Precision | bfloat16 |
+| Hardware | 4 × A100-80 GB |
+
+---
+
+## ✅ Intended Use
+* YOLO 检出 → 结构化缺陷描述  
+* 生成整改建议 / 工单标题 / 优先级  
+* 巡检知识库问答（RAG + Ollama）
+
+### ❌ Out-of-scope
+* 医疗 / 法律结论  
+* 任何未经人工复核的安全决策
+
+---
+
+## ⚠️ Limitations
+* 8 B 参数 ≠ GPT-4 级别推理深度  
+* 训练域集中在隧道场景，泛化到其他土木结构有限  
+* 多语种（非中英）支持较弱
+
+---
+
+## 📄 Citation
+
+```text
+@misc{mozihe2025agvllm,
+  title   = {AGV-LLM: A Domain LLM for Tunnel Inspection},
+  author  = {Zhu, Junheng},
+  year    = {2025},
+  url     = {https://huggingface.co/mozihe/agv_llm}
+}
+```
+
+---
+
+## 📝 License
+Apache 2.0 — 商用、私有部署皆可，保留版权与许可证即可。  
+若本模型帮你省掉一张加班单，欢迎 ⭐！