Instructions to use thefabdev/llama-3-docker-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thefabdev/llama-3-docker-ft with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="thefabdev/llama-3-docker-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("thefabdev/llama-3-docker-ft")
model = AutoModelForCausalLM.from_pretrained("thefabdev/llama-3-docker-ft")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use thefabdev/llama-3-docker-ft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thefabdev/llama-3-docker-ft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thefabdev/llama-3-docker-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/thefabdev/llama-3-docker-ft

SGLang

How to use thefabdev/llama-3-docker-ft with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "thefabdev/llama-3-docker-ft" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thefabdev/llama-3-docker-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "thefabdev/llama-3-docker-ft" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thefabdev/llama-3-docker-ft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use thefabdev/llama-3-docker-ft with Docker Model Runner:
```
docker model run hf.co/thefabdev/llama-3-docker-ft
```

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.

Downloads last month: -

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for thefabdev/llama-3-docker-ft

Base model

meta-llama/Meta-Llama-3-8B-Instruct

Adapter

(1177)

this model

thefabdev
/

llama-3-docker-ft