
微调 Llama 3:针对您用例的实用指南
微调 Meta Llama 3 模型的实操指南——涵盖模型选择、数据集准备、LoRA 配置、训练技巧以及部署为 GGUF 进行本地推理。
Llama 3 是最强大的开源模型系列之一。其强大的基线性能、宽松许可和广泛的社区支持使其成为大多数微调项目的默认起点。
选择正确的 Llama 3 变体
Llama 3 8B
主力变体。适合分类、提取、结构化输出、初次微调。16GB VRAM 即可用 LoRA 微调。
Llama 3 70B
重量级。适合复杂推理、长文本生成。需要多 GPU 设置。
建议:从 Llama 3 8B 开始。
LoRA 配置推荐
| 参数 | 值 |
|---|---|
| LoRA rank | 16 |
| LoRA alpha | 32 |
| 目标模块 | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| 学习率 | 2e-4 |
| Epochs | 3 |
预期改善
| 指标 | 基础 Llama 3 8B(提示) | 微调 Llama 3 8B |
|---|---|---|
| 任务准确率 | 60-75% | 85-95% |
| 格式一致性 | 70-80% | 95-99% |
| 幻觉率 | 10-20% | 2-5% |
部署
cat > Modelfile << 'EOF'
FROM ./llama-3-8b-my-task.gguf
SYSTEM "You are a medical coding assistant."
PARAMETER temperature 0.1
EOF
ollama create medical-coder -f Modelfile
ollama run medical-coder
早鸟价:$14.50/月终身锁定——上线后涨至 $34.50/月。加入候补名单 →
延伸阅读
Ship AI that runs on your users' devices.
Early bird pricing starts at $14.50/mo — locked in for life. Plans for builders and agencies.
Keep reading

How to Fine-Tune an LLM: The Complete 2026 Guide
Learn how to fine-tune large language models step by step — from preparing training data and choosing a base model to configuring LoRA, evaluating results, and deploying locally.

Fine-Tuning for App Developers: A Non-ML-Engineer's Guide
A practical guide to fine-tuning AI models for mobile app developers. Learn LoRA, QLoRA, and GGUF export without needing an ML background.

Distilling Claude/GPT into a 7B Model for Production: Step-by-Step
A step-by-step tutorial for distilling the capabilities of Claude or GPT-4o into a 7B parameter model for local production deployment — from dataset generation through fine-tuning to GGUF export.