llama-3-docker-ft/README.md

library_name, base_model, license, datasets, language, tags, pipeline_tag

library_name	base_model	license	datasets	language	tags	pipeline_tag
transformers	meta-llama/Meta-Llama-3-8B-Instruct	llama3	MattCoddity/dockerNLcommands	en	lora docker text-generation	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), 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 from the base model

Training Data

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.