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mlx-community/llm-jp-4-8b-thinking-4bit
llm-jp-4-8b-thinking-4bit is a text generation model from mlx-community. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
This model mlx-community/llm-jp-4-8b-thinking-4bit was converted to MLX format from llm-jp/llm-jp-4-8b-thinking using mlx-lm version 0.31.1.
Downloads · 30 days
420
19% of all-time downloads
All-time downloads
2.3K
Public
Parameters
8.6B
4.8 GB on disk
Likes
2
Public
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.safetensors4.8 GB · 100%
How the weights are stored.
U328.6B · 100%
From the Hugging Face model README
This model mlx-community/llm-jp-4-8b-thinking-4bit was converted to MLX format from llm-jp/llm-jp-4-8b-thinking using mlx-lm version 0.31.1.
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/llm-jp-4-8b-thinking-4bit")
prompt = "こんにちは"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_dict=False,
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
pip install git+https://github.com/waybarrios/vllm-mlx.git
vllm-mlx serve mlx-community/llm-jp-4-8b-thinking-4bit --reasoning-parser gpt_oss --port 8000
vllm-mlx-chat --server-url http://localhost:8000
or,
from openai import OpenAI
# Without API key (local development)
client = OpenAI(base_url="http://localhost:8000/v1", api_key="not-needed")
response = client.chat.completions.create(
model="default",
messages=[{"role": "user", "content": "こんにちは"}],
)
print(response.choices[0].message.content)