LFM2.5 1.2B Instruct Alternatives

LFM2.5-1.2B is a compact 1.2 billion parameter instruct model from LiquidAI, distributed in GGUF format for easy use with llama.cpp and other local inference engines. It is designed for efficient on-device or CPU-based text generation and instruction following. The model balances performance and resource usage for local deployment scenarios.

LFM2-1.2B is a 1.2 billion parameter model developed by Liquid AI. It features a custom chat template supporting tool lists and system prompts. The model is designed for efficient local or cloud inference and is suitable for conversational AI and agentic applications.

LFM2.5-8B-A1B is an 8 billion parameter language model published by LiquidAI on the Hugging Face platform. It belongs to the LFM series of foundation models and is offered for download and local execution through the Hugging Face ecosystem. The model includes a defined chat template in Jinja format along with a specific tokenizer configuration. This configuration sets the beginning of sequence token to "<|im_start|", the end of sequence token to "<|im_end|", a pad token of "<|pad|", and disables the default system prompt. The template supports conditional preservation of thinking steps and includes macros for formatting argument values and parsing multimodal content that can handle text strings, image placeholders, and JSON-serialized items. It is delivered as a repository on Hugging Face, where users can access the model weights and associated files for integration with the Transformers library or compatible inference engines. The surrounding platform provides access to models, datasets, and community resources under an emphasis on open source and open science. No pricing, licensing terms, or additional capabilities are stated in the repository metadata.

LFM2.5-1.2B-Instruct-MLX-6bit is a 6-bit quantized version of a 1.2 billion parameter instruction-tuned language model optimized for the MLX framework on Apple silicon. It includes a chat template and supports tool use. The model is distributed on Hugging Face for local inference on Macs and is suitable for developers seeking lightweight on-device AI capabilities.

LFM2.5-1.2B-Instruct-MLX-8bit is a small instruction-tuned language model provided in an 8-bit quantized format optimized for the MLX framework on Apple devices. It supports tool use and system prompts. The model is hosted on Hugging Face for easy integration into local AI applications and experimentation.

LFM2.5-VL-1.6B-GGUF is a quantized GGUF version of Liquid AI's 1.6 billion parameter vision-language foundation model. It supports multimodal inputs including images and text, with a chat template optimized for local execution using tools like llama.cpp. Designed for efficient on-device or local server inference without requiring high-end GPUs.

A 4-bit quantized version of the LFM2.5 1.2B Instruct model optimized for the MLX framework on Apple Silicon. It is designed for local inference on Macs and includes a chat template suitable for instruction following. The model is distributed via Hugging Face for use with MLX and LM Studio.

SmolLM2-1.7B-Instruct is a 1.7 billion parameter language model developed by Hugging Face for instruction following and chat use cases. It uses a specific chat template and is designed to be a helpful AI assistant. The model is compact enough for local deployment while offering strong performance for its size. It is part of the SmolLM2 model family.

This is a quantized (5-bit) version of the LFM2-24B model in MLX format, suitable for efficient inference on Apple devices. It includes a chat template and is distributed via Hugging Face for use with MLX and related tools.

This is a quantized (6-bit) version of the LFM2-24B model in MLX format, suitable for efficient inference on Apple devices. It includes a chat template and is distributed via Hugging Face for use with MLX and related tools.