Instructions to use chuanli11/Llama-3.2-3B-Instruct-uncensored with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chuanli11/Llama-3.2-3B-Instruct-uncensored with Transformers:

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

pipe = pipeline("text-generation", model="chuanli11/Llama-3.2-3B-Instruct-uncensored")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chuanli11/Llama-3.2-3B-Instruct-uncensored")
model = AutoModelForCausalLM.from_pretrained("chuanli11/Llama-3.2-3B-Instruct-uncensored", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chuanli11/Llama-3.2-3B-Instruct-uncensored with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chuanli11/Llama-3.2-3B-Instruct-uncensored"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chuanli11/Llama-3.2-3B-Instruct-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chuanli11/Llama-3.2-3B-Instruct-uncensored

SGLang

How to use chuanli11/Llama-3.2-3B-Instruct-uncensored 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 "chuanli11/Llama-3.2-3B-Instruct-uncensored" \
    --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": "chuanli11/Llama-3.2-3B-Instruct-uncensored",
		"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 "chuanli11/Llama-3.2-3B-Instruct-uncensored" \
        --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": "chuanli11/Llama-3.2-3B-Instruct-uncensored",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chuanli11/Llama-3.2-3B-Instruct-uncensored with Docker Model Runner:
```
docker model run hf.co/chuanli11/Llama-3.2-3B-Instruct-uncensored
```

Just wanted to thank you for sharing this fantastic model with the community! Much appreciated! 🙏

by JosefAlbers - opened Nov 2, 2024

Discussion

JosefAlbers

Nov 2, 2024

I've been exploring different models and came across yours. Really impressed by what you've created here and wanted to express my appreciation for making it available to everyone. Thank you for the contribution, I am really enjoying this finetune.

JosefAlbers changed discussion status to closed Nov 2, 2024

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