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--- |
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license: apache-2.0 |
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tags: |
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- llm |
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- mozihe |
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- agv |
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- defect-detection |
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- chinese |
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- english |
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- transformers |
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- ollama |
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model_name: agv_llm |
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base_model: meta-llama/Llama-3.1-8B |
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library_name: transformers |
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--- |
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# 📄 AGV-LLM |
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> **Small enough to self-host, smart enough to 写巡检报告，分析缺陷数据** |
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> 8 B bilingual model fine-tuned for **tunnel-defect description** & **work-order drafting**. |
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> Works in both **Transformers** and **Ollama**. |
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--- |
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## ✨ Highlights |
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| Feature | Details | |
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| ------- | ------- | |
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| 🔧 **Domain-specific** | 56 K 巡检对话 / 工单指令数据 / 数据分析 | |
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| 🧑🏫 **LoRA fine-tuned** | QLoRA-NF4, Rank 8, α = 16 | |
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| 🈶 **Bilingual** | 中文 ↔ English | |
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| ⚡ **Fast** | ~15 tok/s on RTX 4090 (fp16) | |
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| 📦 **Drop-in** | `AutoModelForCausalLM` **or** `ollama pull mozihe/agv_llm` | |
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--- |
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## 🛠️ Usage |
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### Transformers |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch, textwrap |
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tok = AutoTokenizer.from_pretrained("mozihe/agv_llm") |
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model = AutoModelForCausalLM.from_pretrained( |
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"mozihe/agv_llm", torch_dtype=torch.float16, device_map="auto" |
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) |
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prompt = ( |
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"请根据以下检测框信息，生成缺陷描述和整改建议：\\n" |
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"位置：x=12.3,y=1.2,z=7.8\\n种类：裂缝\\n置信度：0.87" |
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) |
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inputs = tok(prompt, return_tensors="pt").to(model.device) |
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out = model.generate(**inputs, max_new_tokens=256, temperature=0.3) |
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print(textwrap.fill(tok.decode(out[0], skip_special_tokens=True), 80)) |
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``` |
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### Ollama |
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1. 构建本地模型并命名： |
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```bash |
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ollama create agv_llm -f Modelfile |
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``` |
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2. 运行： |
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``` |
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ollama run agv_llm |
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``` |
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> 说明 |
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> - `ADAPTER` 行既支持远程 Hugging Face 路径，也支持 `file://` 本地 .safetensors。 |
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> - 更多 Modelfile 指令见 <https://github.com/ollama/ollama/blob/main/docs/modelfile.md> |
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--- |
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## 📚 Training Details |
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| Item | Value | |
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| ---- | ----- | |
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| Base | Llama-3.1-8B | |
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| Method | QLoRA (bitsandbytes NF4) | |
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| Steps | 25 epochs | |
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| LR / Scheduler | 1e-4 / cosine | |
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| Context | 4 096 tokens | |
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| Precision | bfloat16 | |
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| Hardware | 4 × A100-80 GB | |
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--- |
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## ✅ Intended Use |
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* YOLO 检出 → 结构化缺陷描述 |
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* 生成整改建议 / 工单标题 / 优先级 |
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* 巡检知识库问答（RAG + Ollama） |
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### ❌ Out-of-scope |
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* 医疗 / 法律结论 |
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* 任何未经人工复核的安全决策 |
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--- |
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## ⚠️ Limitations |
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* 8 B 参数 ≠ GPT-4 级别推理深度 |
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* 训练域集中在隧道场景，泛化到其他土木结构有限 |
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* 多语种（非中英）支持较弱 |
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--- |
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## 📄 Citation |
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```text |
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@misc{mozihe2025agvllm, |
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title = {AGV-LLM: A Domain LLM for Tunnel Inspection}, |
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author = {Zhu, Junheng}, |
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year = {2025}, |
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url = {https://huggingface.co/mozihe/agv_llm} |
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} |
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``` |
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--- |
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## 📝 License |
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Apache 2.0 — 商用、私有部署皆可，保留版权与许可证即可。 |
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若本模型帮你省掉一次组会汇报（不包ppt），欢迎 ⭐！ |
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