--- library_name: mlx license: other license_name: lfm1.0 license_link: LICENSE language: - en - ja - ko - fr - es - de - it - pt - ar - zh pipeline_tag: text-generation tags: - liquid - lfm2.5 - edge - mlx base_model: LiquidAI/LFM2.5-1.2B-Instruct ---
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# LFM2.5-1.2B-Instruct-8bit MLX export of [LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) for Apple Silicon inference. ## Model Details | Property | Value | |----------|-------| | Parameters | 1.2B | | Precision | 8-bit | | Group Size | 64 || Size | 1.2 GB | | Context Length | 128K | ## Recommended Sampling Parameters | Parameter | Value | |-----------|-------| | temperature | 0.1 | | top_k | 50 | | top_p | 0.1 | | repetition_penalty | 1.05 | | max_tokens | 512 | ## 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-8bit") 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](LICENSE).