--- library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language: - en - ar - zh - fr - de - ja - ko - es - pt pipeline_tag: text-generation tags: - liquid - unsloth - lfm2.5 - edge base_model: - LiquidAI/LFM2.5-8B-A1B --- ## Updated from Liquid's chat template update

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

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# LFM2.5-8B-A1B LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. - **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices. - **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks. - **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang. Find more information about LFM2.5-8B-A1B in our [blog post](https://www.liquid.ai/blog/lfm2-5-8b-a1b). ![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/qUZVGkns1bg3sZUShBbhv.png) **AA-Omniscience Index (higher is better) rewards correct answers and penalizes hallucinations. Scores range from -100 to 100. See more results on [Artificial Analysis](https://artificialanalysis.ai/evaluations/omniscience).* ## 🗒️ Model Details | Model | Parameters | Description | | --- | --- | --- | | [LFM2.5-8B-A1B-Base](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base) | 8.3B total / 1.5B active | Pre-trained base model for fine-tuning | | [**LFM2.5-8B-A1B**](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) | 8.3B total / 1.5B active | Reasoning-tuned general-purpose model | LFM2.5-8B-A1B is a general-purpose text-only model with the following features: - **Total parameters**: 8.3B - **Active parameters**: 1.5B - **Number of layers**: 24 (18 double-gated LIV conv + 6 GQA) - **Training budget**: 38 trillion tokens - **Context length**: 131,072 - **Vocabulary size**: 128,000 - **Languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish - **Generation parameters**: We recommend the following parameters: - `temperature: 0.2` - `top_k: 80` - `repetition_penalty: 1.05` | Model | Description | | --- | --- | | [**LFM2.5-8B-A1B**](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) | Original model checkpoint in native format. Best for fine-tuning or inference with Transformers, vLLM, and SGLang. | | [LFM2.5-8B-A1B-GGUF](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-GGUF) | Quantized format for llama.cpp and compatible tools. Optimized for edge inference and local deployment. | | [LFM2.5-8B-A1B-ONNX](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-ONNX) | ONNX Runtime format for cross-platform deployment. | | [LFM2.5-8B-A1B-MLX](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-MLX-8bit) | MLX format for Apple Silicon. Optimized for fast inference on Mac devices. | We recommend using LFM2.5-8B-A1B for agentic workflows, tool use, structured outputs, multilingual assistants, and on-device personal-assistant applications. It is not the best fit for heavy programming or knowledge-intensive question answering without retrieval. ### Chat Template LFM2.5 uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template) 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 ``` Because LFM2.5-8B-A1B is a reasoning model, assistant turns contain an explicit chain of thought before the final answer. You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically. ### Tool Use LFM2.5 supports function calling in four steps: 1. **Function definition**: Provide the list of tools as a JSON object in the system prompt, or use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) with `tools=...`. 2. **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. 3. **Function execution**: Execute the call and return the result with the `tool` role. 4. **Final answer**: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt. See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) 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|> ``` ## 🏃 Inference LFM2.5-8B-A1B is supported by many inference frameworks. See the [Inference documentation](https://docs.liquid.ai/lfm/inference/transformers) for the full list. | Name | Description | Docs | Notebook | |------|-------------|------|:--------:| | [Transformers](https://github.com/huggingface/transformers) | Simple inference with direct access to model internals. | Link |

| | [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | Link | — | | [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | Link | — | Quick start with Transformers (compatible with `transformers>=5.0.0`): ```python from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer model_id = "LiquidAI/LFM2.5-8B-A1B" 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, ).to(model.device) output = model.generate( input_ids, do_sample=True, temperature=0.2, top_k=80, repetition_penalty=1.05, max_new_tokens=8192, 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](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | Link |

| ## 📊 Performance ### Improvements over LFM2-8B-A1B Thanks to reasoning, scaled-up pre-training, and large-scale RL, LFM2.5-8B-A1B improves over its predecessor across the board: | Benchmark | LFM2-8B-A1B | LFM2.5-8B-A1B | Δ | | :--- | ---: | ---: | ---: | | AA-Omniscience Index | -78.42 | -24.70 | +53.62 | | AA-Omniscience Accuracy | 7.33 | 8.67 | +1.34 | | AA-Omniscience Non-Hallucination Rate | 7.46 | 63.47 | +56.01 | | IFEval | 79.44 | 91.84 | +12.40 | | IFBench | 26.00 | 56.47 | +30.47 | | Multi-IF | 58.54 | 79.93 | +21.39 | | MATH500 | 74.80 | 88.76 | +13.96 | | AIME25 | 20.00 | 42.53 | +22.53 | | BFCLv3 | 45.07 | 64.36 | +19.29 | | BFCLv4 | 25.52 | 48.50 | +22.98 | | Tau² Telecom | 13.60 | 88.07 | +74.47 | | Tau² Retail | 7.02 | 39.82 | +32.80 | ### Knowledge and instruction following | Model | Parameters | AA-Omni. Index | AA-Omni. Accuracy | AA-Omni. Non-Halluc. | IFEval | IFBench | Multi-IF | | :--- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | | LFM2.5-8B-A1B | 8B/A1B | -24.70 | 8.67 | 63.47 | 91.84 | 56.47 | 79.93 | | | Granite-4.0-H-Tiny | 7B/A1B | -75.50 | 9.37 | 6.38 | 82.23 | 21.28 | 59.00 | | | Qwen3.5-4B | 4B | -51.53 | 17.20 | 16.99 | 87.80 | 50.38 | 67.43 | | | Qwen3-30B-A3B-Thinking-2507 | 30.5B/3.3B | -51.31 | 18.80 | 13.87 | 90.82 | 51.11 | 79.04 | | | Gemma-4-E2B-IT | 5.1B | -72 | 7.00 | 15.05 | 82.93 | 33.53 | 69.70 | | | Gemma-4-E4B-IT | 8B | -50.67 | 8.10 | 36.06 | 87.74 | 39.48 | 77.58 | | | Gemma-4-26B-A4B-IT | 26B/4B | -62.07 | 14.37 | 10.75 | 91.40 | 47.25 | 82.06 | | | gpt-oss-20b | 21B/3.6B | -49.17 | 14.57 | 24.50 | 86.73 | 58.65 | 76.64 | | ### Math and agentic workflows | Model | Parameters | MATH500 | AIME25 | AIME26 | BFCLv3 | BFCLv4 | Tau² Telecom | Tau² Retail | |---|---|---|---|---|---|---|---|---| | LFM2.5-8B-A1B | 8B/A1B | 88.76 | 42.53 | 50.00 | 64.79 | 49.73 | 88.07 | 39.82 | | Granite-4.0-H-Tiny | 7B/A1B | 59.20 | 4.93 | 3.33 | 56.89 | 28.52 | 16.67 | 18.42 | | Qwen3.5-4B | 4B | 80.76 | 54.28 | 58.33 | 71.06 | 54.01 | 87.72 | 71.93 | | Qwen3-30B-A3B-Thinking-2507 | 30.5B/3.3B | 86.48 | 71.67 | 66.67 | 73.39 | 50.53 | 21.93 | 56.14 | | Gemma-4-E2B-IT | 5.1B | 64.00 | 26 | 30 | 56.44 | 31.91 | 22.37 | 18.95 | | Gemma-4-E4B-IT | 8B | 65.00 | 34.33 | 40.67 | 57.31 | 33.92 | 26.75 | 42.11 | ### CPU Inference ![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/yWAChLNCguGTl9lXBL47p.png) ### GPU Inference LFM2.5-8B-A1B is the fastest model in its size class, reaching **18.5K output tokens per second at high concurrency**, over 1.6B tokens per day on a single H100. ![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/LX3oIXQeDm51eaLQs64an.png) ## 📬 Contact - Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai). - If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). ## Citation ```bibtex @article{liquidAI20268BA1B, author = {Liquid AI}, title = {LFM2.5-8B-A1B: Personal Assistant On Your Laptop}, journal = {Liquid AI Blog}, year = {2026}, note = {www.liquid.ai/blog/lfm2-5-8b-a1b}, } ``` ```bibtex @article{liquidai2025lfm2, title = {LFM2 Technical Report}, author = {Liquid AI}, journal = {arXiv preprint arXiv:2511.23404}, year = {2025} } ```