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Model: thefabdev/llama-3-docker-ft
Source: Original Platform

README.md

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+---
+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.