README.md

---
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.