Instructions to use LiquidAI/LFM2.5-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-350M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-350M")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-350M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LiquidAI/LFM2.5-350M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-350M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-350M

SGLang

How to use LiquidAI/LFM2.5-350M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LiquidAI/LFM2.5-350M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LiquidAI/LFM2.5-350M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LiquidAI/LFM2.5-350M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-350M with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-350M
```

LFM2.5-350M / README.md

mlabonne

Update README.md

edba0e7 verified 2 months ago

preview code

raw

history blame

14.1 kB

	---
	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
	- lfm2.5
	- edge
	base_model: LiquidAI/LFM2.5-350M-Base
	---

	<div align="center">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.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/getting-started/welcome"><strong>Docs</strong></a> •
	<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •
	<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</div>

	# LFM2.5-350M

	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.

	- Best-in-class performance: A 350M model rivaling much larger models, bringing high-quality AI to your pocket.
	- Fast edge inference: 313 tok/s decode on AMD CPU, 188 tok/s on Snapdragon Gen4. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM.
	- Scaled training: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.

	Find more information about LFM2.5-350M in our [blog post](https://www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind).

	> [!NOTE]
	> 💻 Demo: https://huggingface.co/spaces/webml-community/lfm2.5-webgpu-summarizer

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/mx39JYUuCa1ehaucRFT7d.png)

	## 🗒️ Model Details

	\| Model \| Parameters \| Description \|
	\|-------\|------------\|-------------\|
	\| [LFM2.5-350M-Base](https://huggingface.co/LiquidAI/LFM2.5-350M-Base) \| 350M \| Pre-trained base model for fine-tuning \|
	\| [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) \| 350M \| General-purpose instruction-tuned model \|

	LFM2.5-350M is a general-purpose text-only model with the following features:

	- Number of parameters: 350M
	- Number of layers: 16 (10 double-gated LIV convolution blocks + 6 GQA blocks)
	- Training budget: 28T tokens
	- Context length: 32,768 tokens
	- Vocabulary size: 65,536
	- Knowledge cutoff: Mid-2024
	- Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, Spanish
	- Generation parameters:
	- `temperature: 0.1`
	- `top_k: 50`
	- `repetition_penalty: 1.05`

	\| Model \| Description \|
	\|-------\|-------------\|
	\| [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) \| Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. \|
	\| [LFM2.5-350M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-350M-GGUF) \| Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. \|
	\| [LFM2.5-350M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-350M-ONNX) \| ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). \|
	\| [LFM2.5-350M-MLX](https://huggingface.co/LiquidAI/LFM2.5-350M-MLX-8bit) \| MLX format for Apple Silicon. Optimized for fast inference on Mac devices using the MLX framework. \|

	We recommend using it for data extraction, structured outputs, and tool use. It is not recommended for knowledge-intensive tasks and programming.


	### 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
	```

	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 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, LFM2.5 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: The function call is executed, and the result is returned as a "tool" role.
	4. Final answer: LFM2 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

	LFM2.5 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/LFM2.5-350M"
	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

	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. \| <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

	### Benchmarks

	\| Model \| GPQA Diamond \| MMLU-Pro \| IFEval \| IFBench \| Multi-IF \|
	\|---\|---\|---\|---\|---\|---\|
	\| LFM2.5-350M \| 30.64 \| 20.01 \| 76.96 \| 40.69 \| 44.92 \|
	\| LFM2-350M \| 27.58 \| 19.29 \| 64.96 \| 18.20 \| 32.92 \|
	\| Granite 4.0-H-350M \| 22.32 \| 13.14 \| 61.27 \| 17.22 \| 28.70 \|
	\| Granite 4.0-350M \| 25.91 \| 12.84 \| 53.48 \| 15.98 \| 24.21 \|
	\| Qwen3.5-0.8B (Instruct) \| 27.41 \| 37.42 \| 59.94 \| 22.87 \| 41.68 \|
	\| Qwen3.5-0.8B (Thinking) \| 19.29 \| -* \| 32.93 \| 22.00 \| 26.44 \|
	\| Gemma 3 1B IT \| 23.89 \| 14.04 \| 63.49 \| 20.33 \| 44.25 \|

	\| Model \| CaseReportBench \| BFCLv3 \| BFCLv4 \| τ²-Bench Telecom \| τ²-Bench Retail \|
	\|---\|---\|---\|---\|---\|---\|
	\| LFM2.5-350M \| 32.45 \| 44.11 \| 21.86 \| 18.86 \| 17.84 \|
	\| LFM2-350M \| 11.67 \| 22.95 \| 12.29 \| 10.82 \| 5.56 \|
	\| Granite 4.0-H-350M \| 12.44 \| 43.07 \| 13.28 \| 13.74 \| 6.14 \|
	\| Granite 4.0-350M \| 0.84 \| 39.58 \| 13.73 \| 2.92 \| 6.14 \|
	\| Qwen3.5-0.8B (Instruct) \| 13.83 \| 35.08 \| 18.70 \| 12.57 \| 6.14 \|
	\| Qwen3.5-0.8B (Thinking) \| 0.39 \| 39.64 \| 25.39 \| 14.33 \| 7.02 \|
	\| Gemma 3 1B IT \| 2.28 \| 16.61 \| 7.17 \| 9.36 \| 6.43 \|

	<i>*Evaluation could not be completed due to doom looping.</i>

	### CPU Inference

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/tlt5UmogSZjbMGC6YEYuO.png)

	### GPU Inference

	![](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/1vzwlXxvFr8lZWmu5jDSx.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{liquidAI2026350M,
	author = {Liquid AI},
	title = {LFM2.5-350M: No Size Left Behind},
	journal = {Liquid AI Blog},
	year = {2026},
	note = {www.liquid.ai/blog/lfm2-5-350m-no-size-left-behind},
	}
	```
	```bibtex
	@article{liquidai2025lfm2,
	title={LFM2 Technical Report},
	author={Liquid AI},
	journal={arXiv preprint arXiv:2511.23404},
	year={2025}
	}
	```