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LiquidAI/LFM2.5-1.2B-Instruct-MLX-5bit

sourceHugging Faceotherupdated 1mo agoView on Hugging Face
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Model Card

<div align="center"> <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" alt="Liquid AI" style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;" /> <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;"> <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm"><strong>Documentation</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> </div> </div>

LFM2.5-1.2B-Instruct-5bit

MLX export of LFM2.5-1.2B-Instruct for Apple Silicon inference.

Model Details

PropertyValue
Parameters1.2B
Precision5-bit
Group Size64Size768 MB
Context Length128K

Recommended Sampling Parameters

ParameterValue
temperature0.1
top_k50
top_p0.1
repetition_penalty1.05
max_tokens512

Use with mlx

bash
pip install mlx-lm
python
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("LiquidAI/LFM2.5-1.2B-Instruct-5bit")

prompt = "What is the capital of France?"

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

sampler = make_sampler(temp=0.1, top_k=50, top_p=0.1)
logits_processors = make_logits_processors(repetition_penalty=1.05)

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=512,
    sampler=sampler,
    logits_processors=logits_processors,
    verbose=True,
)

License

This model is released under the LFM 1.0 License.