---
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
---
# 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. | Link | 
|
| [vLLM](https://github.com/vllm-project/vllm) | High-throughput production deployments with GPU. | Link | |
| [llama.cpp](https://github.com/ggml-org/llama.cpp) | Cross-platform inference with CPU offloading. | 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 | — |
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. | Link | |
| CPT ([Unsloth](https://github.com/unslothai/unsloth)) | Continued Pre-Training using Unsloth for translation. | Link | |
| SFT ([Unsloth](https://github.com/unslothai/unsloth)) | Supervised Fine-Tuning with LoRA using Unsloth. | Link | |
| SFT ([TRL](https://github.com/huggingface/trl)) | Supervised Fine-Tuning with LoRA using TRL. | Link | |
| DPO ([TRL](https://github.com/huggingface/trl)) | Direct Preference Optimization with LoRA using TRL. | Link | |
| GRPO ([Unsloth](https://github.com/unslothai/unsloth)) | GRPO with LoRA using Unsloth. | Link | |
| GRPO ([TRL](https://github.com/huggingface/trl)) | GRPO with LoRA using TRL. | Link | |
## 📊 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}
}
```