Instructions to use LiquidAI/LFM2.5-8B-A1B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2.5-8B-A1B-Base with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2.5-8B-A1B-Base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2.5-8B-A1B-Base")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2.5-8B-A1B-Base")
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/LFM2.5-8B-A1B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2.5-8B-A1B-Base"
# 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/LFM2.5-8B-A1B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2.5-8B-A1B-Base

SGLang

How to use LiquidAI/LFM2.5-8B-A1B-Base 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/LFM2.5-8B-A1B-Base" \
    --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/LFM2.5-8B-A1B-Base",
		"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/LFM2.5-8B-A1B-Base" \
        --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/LFM2.5-8B-A1B-Base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2.5-8B-A1B-Base with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2.5-8B-A1B-Base
```

LFM2.5-8B-A1B-Base / README.md

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	---
	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
	---

	<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>


	# LFM2.5-8B-A1B-Base

	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/F_rR3bNCHLQIx7TKVqe8U.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_p: 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) \| 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.

	## 🏃 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. \| <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> \| — \|

	Quick start with Transformers (compatible with `transformers>=5.0.0`):

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer

	model_id = "LiquidAI/LFM2.5-8B-A1B-Base"
	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. \| <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> \|

	## 📬 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}
	}
	```