Instructions to use Bruce1489/Llama3.2-docker-command-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Bruce1489/Llama3.2-docker-command-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Bruce1489/Llama3.2-docker-command-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Bruce1489/Llama3.2-docker-command-v2")
model = AutoModelForCausalLM.from_pretrained("Bruce1489/Llama3.2-docker-command-v2")
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

vLLM

How to use Bruce1489/Llama3.2-docker-command-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Bruce1489/Llama3.2-docker-command-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Bruce1489/Llama3.2-docker-command-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Bruce1489/Llama3.2-docker-command-v2

SGLang

How to use Bruce1489/Llama3.2-docker-command-v2 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 "Bruce1489/Llama3.2-docker-command-v2" \
    --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": "Bruce1489/Llama3.2-docker-command-v2",
		"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 "Bruce1489/Llama3.2-docker-command-v2" \
        --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": "Bruce1489/Llama3.2-docker-command-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Bruce1489/Llama3.2-docker-command-v2 with Docker Model Runner:
```
docker model run hf.co/Bruce1489/Llama3.2-docker-command-v2
```

Model Card for Model ID

I fine-tuned the "meta-llama/Llama-3.2-1B-Instruct" model using the QLoRA technique.

The resulting model can generate Docker commands based on given statements.

Developed by: Bruce1489(정성훈, Bruce Jung)
License: mit
Finetuned from model [optional]: meta-llama/Llama-3.2-1B-Instruct

Direct Use

import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="Bruce1489/Llama3.2-docker-command-v2",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
messages = [
    {"role": "system", "content": "translate this sentence in docker command"},
    {"role": "user", "content": "Please show me the Docker containers that have exited and are related to the mongo image."},
]

outputs = pipe(
    messages,
    max_new_tokens=256,
    temperature = 0.5,
    top_p  = 0.9,
    top_k = 10,
    do_sample = True,
    repetition_penalty = 1.2
)
print(outputs[0]["generated_text"][-1]['content'])

Downloads last month: 4

Safetensors

Model size

1B params

Tensor type

F32

Bruce1489
/

Llama3.2-docker-command-v2

Model Card for Model ID

I fine-tuned the "meta-llama/Llama-3.2-1B-Instruct" model using the QLoRA technique.

The resulting model can generate Docker commands based on given statements.

Direct Use

Dataset used to train Bruce1489/Llama3.2-docker-command-v2