| | --- |
| | 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 |
| | - lfm2 |
| | - edge |
| | --- |
| | |
| | <div align="center"> |
| | <img |
| | src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/VmrjMZg7WYfMX5YFHuZ-P.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> |
| | |
| | # LFM2-24B-A2B |
| |
|
| | LFM2 is a family of hybrid models designed for on-device deployment. LFM2-24B-A2B is the largest model in the family, scaling the architecture to 24 billion parameters while keeping inference efficient. |
| |
|
| | - **Best-in-class efficiency**: A 24B MoE model with only 2B active parameters per token, fitting in 32 GB of RAM for deployment on consumer laptops and desktops. |
| | - **Fast edge inference**: 112 tok/s decode on AMD CPU, 293 tok/s on H100. Fits in 32B GB of RAM with day-one support llama.cpp, vLLM, and SGLang. |
| | - **Predictable scaling**: Quality improves log-linearly from 350M to 24B total parameters, confirming the LFM2 hybrid architecture scales reliably across nearly two orders of magnitude. |
| |
|
| |  |
| |
|
| | Find more information about LFM2-24B-A2B in our [blog post](https://www.liquid.ai/blog/lfm2-24b-a2b). |
| |
|
| | ## 🗒️ Model Details |
| |
|
| | LFM2-24B-A2B is a general-purpose instruct model (without reasoning traces) with the following features: |
| |
|
| | | Property | [**LFM2-8B-A1B**](https://huggingface.co/LiquidAI/LFM2-8B-A1B) | [**LFM2-24B-A2B**](https://huggingface.co/LiquidAI/LFM2-24B-A2B) | |
| | | --------------------- | ----------------------------- | ----------------------------- | |
| | | **Total parameters** | 8.3B | 24B | |
| | | **Active parameters** | 1.5B | 2.3B | |
| | | **Layers** | 24 (18 conv + 6 attn) | 40 (30 conv + 10 attn) | |
| | | **Context length** | 32,768 tokens | 32,768 tokens | |
| | | **Vocabulary size** | 65,536 | 65,536 | |
| | | **Training precision**| Mixed BF16/FP8 | Mixed BF16/FP8 | |
| | | **Training budget** | 12 trillion tokens | 17 trillion tokens | |
| | | **License** | LFM Open License v1.0 | LFM Open License v1.0 | |
| |
|
| | **Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Spanish, Portuguese |
| |
|
| | **Generation parameters**: |
| | - `temperature: 0.1` |
| | - `top_k: 50` |
| | - `repetition_penalty: 1.05` |
| |
|
| | We recommend the following use cases: |
| |
|
| | - **Agentic tool use**: Native function calling, web search, structured outputs. Ideal as the fast inner-loop model in multi-step agent pipelines. |
| | - **Offline document summarization and Q&A**: Run entirely on consumer hardware for privacy-sensitive workflows (legal, medical, corporate). |
| | - **Privacy-preserving customer support agent**: Deployed on-premise at a company, handles multi-turn support conversations with tool access (database lookups, ticket creation) without data leaving the network. |
| | - **Local RAG pipelines**: Serve as the generation backbone in retrieval-augmented setups on a single machine without GPU servers. |
| |
|
| | ### Chat Template |
| |
|
| | LFM2-24B-A2B 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 |
| | ``` |
| |
|
| | You can use [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating#using-applychattemplate) to format your messages automatically. |
| |
|
| | ### Tool Use |
| |
|
| | LFM2-24B-A2B supports function calling as follows: |
| |
|
| | 1. **Function definition**: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the [`tokenizer.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_extras#passing-tools) function with tools. |
| | 2. **Function call**: By default, LFM2-24B-A2B 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**: The function call is executed, and the result is returned as a "tool" role. |
| | 4. **Final answer**: LFM2-24B-A2B interprets the outcome of the function call to address the original user prompt in plain text. |
| |
|
| | 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 |
| |
|
| | LFM2-24B-A2B 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. | <a href="https://docs.liquid.ai/lfm/inference/transformers">Link</a> | <a href="https://colab.research.google.com/drive/1_q3jQ6LtyiuPzFZv7Vw8xSfPU5FwkKZY?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | <a href="https://docs.liquid.ai/lfm/inference/vllm">Link</a> | <a href="https://colab.research.google.com/drive/1VfyscuHP8A3we_YpnzuabYJzr5ju0Mit?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | <a href="https://docs.liquid.ai/lfm/inference/llama-cpp">Link</a> | <a href="https://colab.research.google.com/drive/1ohLl3w47OQZA4ELo46i5E4Z6oGWBAyo8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | [MLX](https://github.com/ml-explore/mlx) | Apple's machine learning framework optimized for Apple Silicon. | <a href="https://docs.liquid.ai/lfm/inference/mlx">Link</a> | — | |
| | | [LM Studio](https://lmstudio.ai/) | Desktop application for running LLMs locally. | <a href="https://docs.liquid.ai/lfm/inference/lm-studio">Link</a> | — | |
| |
|
| | Here's a quick start example with Transformers: |
| |
|
| | ```python |
| | from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer |
| | |
| | model_id = "LiquidAI/LFM2-24B-A2B" |
| | 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=0.1, |
| | top_k=50, |
| | repetition_penalty=1.05, |
| | max_new_tokens=512, |
| | streamer=streamer, |
| | ) |
| | ``` |
| |
|
| | ## 🔧 Fine-Tuning |
| |
|
| | | Name | Description | Docs | Notebook | |
| | |------|-------------|------|----------| |
| | | CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for text completion. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/10fm7eNMezs-DSn36mF7vAsNYlOsx9YZO?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1gaP8yTle2_v35Um8Gpu9239fqbU7UgY8?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1vGRg4ksRj__6OLvXkHhvji_Pamv801Ss?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | <a href="https://docs.liquid.ai/lfm/fine-tuning/unsloth">Link</a> | <a href="https://colab.research.google.com/drive/1mIikXFaGvcW4vXOZXLbVTxfBRw_XsXa5?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| | | GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | <a href="https://docs.liquid.ai/lfm/fine-tuning/trl">Link</a> | <a href="https://colab.research.google.com/github/Liquid4All/cookbook/blob/main/finetuning/notebooks/grpo_for_verifiable_tasks.ipynb"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> | |
| |
|
| | ## 📊 Performance |
| |
|
| | ### CPU Inference |
| |
|
| | We compared LFM2-24B-A2B against two popular MoE models of similar size: Qwen3-30B-A3B-Instruct-2507 (30.5B total, 3.3B active parameters) and gpt-oss-20b (21B total, 3.6B active parameters). We measured both prefill and decode throughputs with Q4_K_M versions of these models using llama.cpp on AMD Ryzen AI Max+ 395. |
| |
|
| |  |
| |
|
| |  |
| |
|
| | ### GPU Inference |
| |
|
| | We also report throughput (total tokens / wall time) achieved with vLLM on a single H100 SXM5 GPU. |
| |
|
| |  |
| |
|
| | ## Contact |
| |
|
| | For enterprise solutions and edge deployment, contact [sales@liquid.ai](mailto:sales@liquid.ai). |
| |
|
| | ## Citation |
| |
|
| | ```bibtex |
| | @article{liquidAI202624B, |
| | author = {Liquid AI}, |
| | title = {LFM2.5-24B-A2B: Scaling Up the LFM2 Architecture}, |
| | journal = {Liquid AI Blog}, |
| | year = {2026}, |
| | note = {www.liquid.ai/blog/}, |
| | } |
| | ``` |
| |
|
| | ```bibtex |
| | @article{liquidai2025lfm2, |
| | title={LFM2 Technical Report}, |
| | author={Liquid AI}, |
| | journal={arXiv preprint arXiv:2511.23404}, |
| | year={2025} |
| | } |
| | ``` |
