Regarding common practices and considerations for fine-tuning, quantizing, and cross-platform deployment of the LLaMA large model, we look forward to everyone's experiences and suggestions.

26 Tem 20:55

In real-world projects, I generally break down the fine-tuning, quantization, and cross-platform deployment of LLaMA into four stages: **data preparation → training script → quantization compression → multi-platform export**. For data, I first use HuggingFace `datasets` to generate JSONL or Parquet files via `train_test_split`, ensuring each sample has the three fields `instruction`, `input`, and `output`. This allows downstream LoRA/PEFT code to directly call `DataCollatorForSeq2Seq`. During training, I prefer using `peft`'s LoRA (`lora_rank=8`, `lora_alpha=32`, `target_modules=["q_proj","v_proj"]`) combined with `bitsandbytes`' 4-bit AdamW. This setup lets me run 7B–13B parameter models on a single 24GB GPU with a learning rate of 2e-4, batch size 4–8, and 2–3 epochs of gradient accumulation. After fine-tuning, I use `optimum`'s `quantize` interface to apply GPTQ/Auto-AWQ (`bits=4`, `group_size=128`), generating `.safetensors` files saved to `model_quantized/`. Next, I export the model to `torchscript` using `torch.compile` (or `onnxruntime`), then serve it with `torchserve` (CPU/GPU) and `torch2trt` (GPU). For mobile, I convert it to `onnx` and compile via `nnapi`/`coreml`. During deployment, I ensure the `tokenizer` version is consistent, set `pad_token_id` to 0, and run baseline tests across hardware: **CPU: 1–2 tokens/ms**, **GPU: ~10 tokens/ms**, **Mobile: ~5 tokens/ms**. Common pitfalls include NaN after quantization (often due to not disabling `torch.nn.LayerNorm`'s fp16 computation) and serialization inconsistencies across platforms (I recommend calling `model.eval()` and manually `torch.save` before export to verify). I encapsulate these steps into `run_finetune.sh`, `run_quant.sh`, and `run_deploy.sh`, adding `set -e` and log saving to ensure stable performance and resource balance across environments.