mlx-community/Llama-3.2-1B-Instruct-4bit

Model

mlx-community/Llama-3.2-1B-Instruct-4bit

21

4 commits

2 repos using this model

1 linked in READMEs

updated Mar 5, 2025

See the code

README

mlx-community/Llama-3.2-1B-Instruct-4bit

The Model mlx-community/Llama-3.2-1B-Instruct-4bit was converted to MLX format from mlx-community/Llama-3.2-1B-Instruct-bf16 using mlx-lm version 0.21.5.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Llama-3.2-1B-Instruct-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
4-bit
conversational
endpoints_compatible
facebook
llama
llama-3
meta
pytorch
safetensors
text-generation
text-generation-inference
transformers

mlx-community/Llama-3.2-1B-Instruct-4bit

Model

mlx-community/Llama-3.2-1B-Instruct-4bit

21

4 commits

2 repos using this model

1 linked in READMEs

updated Mar 5, 2025

See the code

README

mlx-community/Llama-3.2-1B-Instruct-4bit

The Model mlx-community/Llama-3.2-1B-Instruct-4bit was converted to MLX format from mlx-community/Llama-3.2-1B-Instruct-bf16 using mlx-lm version 0.21.5.

Use with mlx

pip install mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/Llama-3.2-1B-Instruct-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)
4-bit
conversational
endpoints_compatible
facebook
llama
llama-3
meta
pytorch
safetensors
text-generation
text-generation-inference
transformers