LiquidAI/LFM2.5-2.6B · Hugging Face

LFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with a 128K context window and agentic post-training. Best-in-class agent: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks. Agentic reinforcement learning: Trained inside the most popular agentic harnesses to improve compatibility. Efficient inference: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory. Find more information about LFM2.5-2.6B in our blog post. 💻 Demos: Try LFM2.5-2.6B's agentic capabilities in a Hugging Face space without any setup: Research Agent in your browser: helps you research a specific question and generates a summary 🗒 Model Details Model Parameters Description LFM2.5-2.6B-Base 2.6B Pre-trained base model for fine-tuning LFM2.5-2.6B 2.6B Post-trained for agentic workloads LFM2.5-2.6B is a general-purpose text-only model with the following features: Total parameters: 2.69B Number of layers: 30 (22 double-gated short convolution blocks + 8 GQA) Training budget: 34 trillion tokens Vocabulary size: 128,000 Context length: 131,072 tokens Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish Generation parameters: temperature: 0.1 top_k: 50 repetition_penalty: 1.1 Model Description LFM2.5-2.6B Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. LFM2.5-2.6B-GGUF Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. LFM2.5-2.6B-ONNX ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). LFM2.5-2.6B-MLX MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. We recommend using it for agentic workloads, tool use, data extraction, RAG, and long-context workflows. It is not recommended for agentic coding and knowledge-heavy tasks. Chat Template LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example: <|startoftext|><|im_start|>system You are a helpful assistant trained by Liquid AI.<|im_end|> <|im_start|>user What is C. elegans?<|im_end|> <|im_start|>assistant You can use tokenizer.apply_chat_template() to format your messages automatically. 💡 Note: LFM2.5-2.6B is a pure reasoning model that always thinks before it answers. It adds a <think> tag directly in the chat template when starting an assistant answer. Tool Use LFM2.5 supports function calling in four steps: Function definition: Provide the list of tools as a JSON object in the system prompt, or use tokenizer.apply_chat_template() with tools=.... Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt. Function execution: Execute the call and return the result with the tool role. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt. See the Tool Use documentation for the full guide. Example: <|startoftext|><|im_start|>system List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|> <|im_start|>user What is the current status of candidate ID 12345?<|im_end|> <|im_start|>assistant <|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|> <|im_start|>tool [{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|> <|im_start|>assistant The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|> Training LFM2.5-2.6B is pre-trained on ~34T tokens, with a mid-training phase that extends the context window to 128K. Post-training then turns the base model into an agent in four stages: supervised fine-tuning (two rounds), per-domain teacher specialization, multi-domain on-policy distillation, and agentic reinforcement learning. In particular, agentic reinforcement learning allows us to directly train the model inside popular agentic harnesses. It exposes the model to their tools, system prompts, and interaction patterns, helping it work reliably across agent environments. 🏃 Inference LFM2.5 is supported by many inference frameworks. See the Inference documentation for the full list. Name Description Docs Notebook Transformers Simple inference with direct access to model internals. Link vLLM High-throughput production deployments with GPU. Link llama.cpp Cross-platform inference with CPU offloading. Link MLX Apple's machine learning framework optimized for Apple Silicon. Link — LM Studio Desktop application for running LLMs locally. Link — SGLang High-throughput production deployments with GPU. Link - Quick start with Transformers (compatible with transformers>=5.0.0): from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "LiquidAI/LFM2.5-2.6B" model = AutoModelForCausalLM.from_pretrained( model_id, device_map="auto", dtype="bfloat16", # attn_implementation="flash_attention_2" <- uncomment on compatible GPU ) tokenizer = AutoTokenizer.from_pretrained(model_id) streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) prompt = "What is C. elegans?" input_ids = tokenizer.apply_chat_template( [{"role": "user", "content": prompt}], add_generation_prompt=True, return_tensors="pt", tokenize=True, )["input_ids"].to(model.device) output = model.generate( input_ids, do_sample=True, temperature=0.1, top_k=50, repetition_penalty=1.1, max_new_tokens=512, streamer=streamer, ) 🔧 Fine-Tuning We recommend fine-tuning LFM2.5 for your specific use case to achieve the best results. Name Description Docs Notebook CPT (Unsloth) Continued Pre-Training using Unsloth for text completion. Link CPT (Unsloth) Continued Pre-Training using Unsloth for translation. Link SFT (Unsloth) Supervised Fine-Tuning with LoRA using Unsloth. Link SFT (TRL) Supervised Fine-Tuning with LoRA using TRL. Link DPO (TRL) Direct Preference Optimization with LoRA using TRL. Link GRPO (TRL) GRPO with LoRA using TRL. Link 📊 Performance Benchmarks We compared LFM2.5-2.6B with relevant sub-10B models on a diverse suite of benchmarks. Benchmark LFM2.5-2.6B (2.6B) gemma-4-E2B-it (5.1B) gemma-4-E4B-it (8B) Qwen3.5-4B (4.7B) Qwen3.5-9B (9.7B) AA-Omni-Public Index -29.50 -74.47 -49.03 -54.30 -50.43 AA-Omni-Public Acc 8.13 6.37 8.33 17.63 21.30 AA-Omni-Public Non-hallu 59.04 13.67 37.42 12.66 8.84 AIME25 51.87 26.33 34.27 49.33 56.07 LiveCodeBenchv6 59.41 54.92 63.77 60.85 69.86 IFBench 59.17 34.08 39.24 48.40 56.47 Multi-IF 80.07 69.44 77.35 55.67 62.55 IFStruct 85.49 64.85 76.65 36.25 78.50 BFCLv4 56.88 36.98 46.39 50.56 60.13 ToolSandbox 77.83 52.40 65.00 75.55 76.44 τ³-Bench Banking 5.67 3.35 4.12 5.45 5.15 Claw-Eval average (EN) 62.85 53.14 58.02 62.28 66.53 PinchBench 68.22 44.24 55.09 71.26 71.45 BrowseComp+ (OpenClaw) 26.89 8.31 15.90 24.46 27.23 CPU Inference Due to its efficient LFM2 architecture, LFM2.5-2.6B is the fastest model we tested, with decode speeds of 220 tokens/s on an M5 Max and 113 tokens/s on a Ryzen AI Max+ 395. At 30 tokens/s, it allows you to run capable agents even on a phone. GPU Inference LFM2.5-2.6B is the fastest model in its size class, reaching almost 15K output tokens per second at high concurrency, roughly 1.3B tokens per day on a single H100. 📬 Contact Got questions or want to connect? Join our Discord community If you are interested in custom solutions with edge deployment, please contact our sales team. Citation @article{liquidAI202626B, author = {Liquid AI}, title = {LFM2.5-2.6B: Agents Everywhere}, journal = {Liquid AI Blog}, year = {2026}, note = {www.liquid.ai/blog/lfm2-5-2-6b}, } @article{liquidai2025lfm2, title = {LFM2 Technical Report}, author = {Liquid AI}, journal = {arXiv preprint arXiv:2511.23404}, year = {2025} }