| --- |
| library_name: transformers |
| base_model: meta-llama/Meta-Llama-3-8B-Instruct |
| license: llama3 |
| datasets: |
| - MattCoddity/dockerNLcommands |
| language: |
| - en |
| tags: |
| - lora |
| - docker |
| - text-generation |
| pipeline_tag: text-generation |
| --- |
| |
| # llama-3-docker-ft |
|
|
| LoRA fine-tune of `meta-llama/Meta-Llama-3-8B-Instruct` that translates natural-language |
| requests into Docker CLI commands. Merged adapter weights (base + LoRA), not an |
| adapter-only checkpoint. |
|
|
| This is a learning-lab artifact ([source notebook and writeup](https://github.com/Fakorede/llm-alignment-lab/tree/main/01_lora_sft)), |
| not a production model. Treat it as a first fine-tuning exercise, not a benchmarked release. |
|
|
| ## Model Details |
|
|
| - **Base model:** `meta-llama/Meta-Llama-3-8B-Instruct`, loaded in 8-bit (`BitsAndBytesConfig(load_in_8bit=True)`) |
| - **Fine-tuning method:** LoRA (`peft`), `r=16`, `lora_alpha=32`, dropout `0.05`, |
| targeting `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
| (41.9M trainable params, 0.52% of 8.07B total) |
| - **Merged:** LoRA adapter merged into the base weights before push (`merge_and_unload()`) |
| - **License:** inherits the [Llama 3 Community License](https://llama.meta.com/llama3/license/) from the base model |
|
|
| ## Training Data |
|
|
| [`MattCoddity/dockerNLcommands`](https://huggingface.co/datasets/MattCoddity/dockerNLcommands), |
| an instruction/input/output dataset pairing natural-language requests with the |
| corresponding Docker CLI command. Split 80/20 train/validation (seed 42). |
|
|
| ## Training Procedure |
|
|
| `transformers.Trainer` + `TrainingArguments`: batch size 2, gradient accumulation 4 |
| (effective batch size 8), `paged_adamw_8bit`, learning rate `2e-4`, 2 epochs (484 steps), |
| fp16, warmup steps 5, weight decay 0.01. |
|
|
| ### Results |
|
|
| | Metric | Value | |
| |---|---| |
| | Train loss (last logged step) | 0.307 | |
| | Train loss (run average) | 0.461 | |
| | Eval loss | 0.341 | |
| | Train runtime | ~2738s (single A100 80GB) | |
|
|
| ## Uses |
|
|
| **Intended use:** translating short, single-turn natural-language instructions about |
| containers/images into a Docker CLI command. Example: |
|
|
| \`\`\`python |
| import transformers |
| import torch |
|
|
| pipeline = transformers.pipeline( |
| "text-generation", |
| model="thefabdev/llama-3-docker-ft", |
| model_kwargs={"torch_dtype": torch.bfloat16}, |
| device_map="auto", |
| ) |
| |
| messages = [ |
| {"role": "system", "content": "You are a helpful assistant. Translate this sentence in docker command"}, |
| {"role": "user", "content": "Display the information of the last 4 containers."}, |
| ] |
| |
| result = pipeline(messages, max_new_tokens=256, temperature=0.25, top_p=1, repetition_penalty=1.2) |
| print(result[0]["generated_text"][-1]["content"]) |
| # docker ps --last 4 |
| \`\`\` |
|
|
| **Out of scope:** general-purpose assistant use, multi-turn conversation, any |
| command generation where correctness/safety of the resulting shell command isn't |
| independently verified before execution. Generated commands are not validated for |
| safety and should not be run against production systems without review. |
|
|
| ## Limitations |
|
|
| - Fine-tuned on a single small, narrow dataset (Docker CLI only), will not generalize |
| to other CLIs or general instruction-following. |
| - Trained for 2 epochs on ~800 examples; not evaluated against a held-out benchmark |
| beyond the validation split loss above. |
| - No safety/red-teaming evaluation has been performed on this model. |
