Text Generation
Transformers
Safetensors
MLX
English
llama
llama-3
meta
facebook
unsloth
mlx-my-repo
text-generation-inference
4-bit precision
Instructions to use cmcmaster/Llama-3.2-3B-Q4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cmcmaster/Llama-3.2-3B-Q4-mlx")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cmcmaster/Llama-3.2-3B-Q4-mlx") model = AutoModelForCausalLM.from_pretrained("cmcmaster/Llama-3.2-3B-Q4-mlx") - MLX
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("cmcmaster/Llama-3.2-3B-Q4-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- vLLM
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cmcmaster/Llama-3.2-3B-Q4-mlx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmcmaster/Llama-3.2-3B-Q4-mlx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cmcmaster/Llama-3.2-3B-Q4-mlx
- SGLang
How to use cmcmaster/Llama-3.2-3B-Q4-mlx 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 "cmcmaster/Llama-3.2-3B-Q4-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmcmaster/Llama-3.2-3B-Q4-mlx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "cmcmaster/Llama-3.2-3B-Q4-mlx" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cmcmaster/Llama-3.2-3B-Q4-mlx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cmcmaster/Llama-3.2-3B-Q4-mlx to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for cmcmaster/Llama-3.2-3B-Q4-mlx to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cmcmaster/Llama-3.2-3B-Q4-mlx to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="cmcmaster/Llama-3.2-3B-Q4-mlx", max_seq_length=2048, ) - MLX LM
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "cmcmaster/Llama-3.2-3B-Q4-mlx" --prompt "Once upon a time"
- Docker Model Runner
How to use cmcmaster/Llama-3.2-3B-Q4-mlx with Docker Model Runner:
docker model run hf.co/cmcmaster/Llama-3.2-3B-Q4-mlx
cmcmaster/Llama-3.2-3B-Q4-mlx
The Model cmcmaster/Llama-3.2-3B-Q4-mlx was converted to MLX format from unsloth/Llama-3.2-3B using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("cmcmaster/Llama-3.2-3B-Q4-mlx")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
0.5B params
Tensor type
F16
·
U32 ·
Hardware compatibility
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4-bit