Instructions to use LiquidAI/LFM2-1.2B-Tool with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LiquidAI/LFM2-1.2B-Tool with Transformers:

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

pipe = pipeline("text-generation", model="LiquidAI/LFM2-1.2B-Tool")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-1.2B-Tool")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-1.2B-Tool")
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/LFM2-1.2B-Tool with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LiquidAI/LFM2-1.2B-Tool"
# 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/LFM2-1.2B-Tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LiquidAI/LFM2-1.2B-Tool

SGLang

How to use LiquidAI/LFM2-1.2B-Tool 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/LFM2-1.2B-Tool" \
    --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/LFM2-1.2B-Tool",
		"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/LFM2-1.2B-Tool" \
        --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/LFM2-1.2B-Tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LiquidAI/LFM2-1.2B-Tool with Docker Model Runner:
```
docker model run hf.co/LiquidAI/LFM2-1.2B-Tool
```

LFM2-1.2B-Tool / 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
	pipeline_tag: text-generation
	tags:
	- liquid
	- lfm2
	- edge
	base_model: LiquidAI/LFM2-1.2B
	---

	<center>
	<div style="text-align: center;">
	<img
	src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2F2b08LKpev0DNEk6DlnWkY.png%26quot%3B%3C%2Fspan%3E
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	</div>
	<div style="display: flex; justify-content: center; gap: 0.5em;">
	<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>
	</center>

	<br>

	# LFM2-1.2B-Tool

	Based on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-Tool is designed for concise and precise tool calling. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use.

	Use cases:

	- Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency.
	- Real-time assistants in cars, IoT devices, or customer support, where response latency is critical.
	- Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution.

	You can find more information about other task-specific models in this [blog post](https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices).

	## 📄 Model details

	Generation parameters: We recommend using greedy decoding with a `temperature=0`.

	System prompt: The system prompt must provide all the available tools

	Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

	![68d41b9699b7e1fafd645300_Model Library-Prompt + Answer](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2Fdc2-lsJ5PwoA8039z-wW_.png%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L59">
	Tool use: It consists of four main steps:
	1. Function definition: LFM2 takes JSON function definitions as input (JSON objects between `<\|tool_list_start\|>` and `<\|tool_list_end\|>` special tokens), usually in the system prompt
	2. Function call: LFM2 writes Pythonic function calls (a Python list between `<\|tool_call_start\|>` and `<\|tool_call_end\|>` special tokens), as the assistant answer.
	3. Function execution: The function call is executed and the result is returned (string between `<\|tool_response_start\|>` and `<\|tool_response_end\|>` special tokens), as a "tool" role.
	4. Final answer: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.

	Here is a simple example of a conversation using tool use:

	```
	<\|startoftext\|><\|im_start\|>system
	List of tools: <\|tool_list_start\|>[{"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"]}}]<\|tool_list_end\|><\|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
	<\|tool_response_start\|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<\|tool_response_end\|><\|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\|>
	```

	> [!WARNING]
	> ⚠️ The model supports both single-turn and multi-turn conversations.

	## 📈 Performance

	For edge inference, latency is a crucial factor in delivering a seamless and satisfactory user experience. Consequently, while test-time-compute inherently provides more accuracy, it ultimately compromises the user experience due to increased waiting times for function calls.

	Therefore, the goal was to develop a tool calling model that is competitive with thinking models, yet operates without any internal chain-of-thought process.

	![image](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FgdPh7juYTsxc7wpD5iYS_.png%3C%2Fspan%3E)%3C!----%3E%3C%2Ftd%3E%3C%2Ftr%3E%3Ctr id="L91">
	We evaluated each model on a proprietary benchmark that was specifically designed to prevent data contamination. The benchmark ensures that performance metrics reflect genuine tool-calling capabilities rather than memorized patterns from training data.

	## 🏃 How to run

	- Hugging Face: [LFM2-1.2B-Tool](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool)
	- llama.cpp: [LFM2-1.2B-Tool-GGUF](https://huggingface.co/LiquidAI/LFM2-1.2B-Tool-GGUF)
	- LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-1.2b-tool)

	You can use the following Colab notebooks for easy inference and fine-tuning:

	\| Notebook \| Description \| Link \|
	\|-------\|------\|------\|
	\| Inference \| Run the model with Hugging Face's transformers library. \| <a href="https://colab.research.google.com/drive/1_HFBuNROTnI-SSZ2zEpqpjJ6SnrsWCU3?usp=sharing"><img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FvlOyMEjwHa_b_LXysEu2E.png%26quot%3B%3C%2Fspan%3E width="110" alt="Colab link"></a> \|
	\| SFT (TRL) \| Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using TRL. \| <a href="https://colab.research.google.com/drive/1j5Hk_SyBb2soUsuhU0eIEA9GwLNRnElF?usp=sharing"><img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FvlOyMEjwHa_b_LXysEu2E.png%26quot%3B%3C%2Fspan%3E width="110" alt="Colab link"></a> \|
	\| DPO (TRL) \| Preference alignment with Direct Preference Optimization (DPO) using TRL. \| <a href="https://colab.research.google.com/drive/1MQdsPxFHeZweGsNx4RH7Ia8lG8PiGE1t?usp=sharing"><img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FvlOyMEjwHa_b_LXysEu2E.png%26quot%3B%3C%2Fspan%3E width="110" alt="Colab link"></a> \|
	\| SFT (Axolotl) \| Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Axolotl. \| <a href="https://colab.research.google.com/drive/155lr5-uYsOJmZfO6_QZPjbs8hA_v8S7t?usp=sharing"><img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FvlOyMEjwHa_b_LXysEu2E.png%26quot%3B%3C%2Fspan%3E width="110" alt="Colab link"></a> \|
	\| SFT (Unsloth) \| Supervised Fine-Tuning (SFT) notebook with a LoRA adapter using Unsloth. \| <a href="https://colab.research.google.com/drive/1HROdGaPFt1tATniBcos11-doVaH7kOI3?usp=sharing"><img src="/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F61b8e2ba285851687028d395%2FvlOyMEjwHa_b_LXysEu2E.png%26quot%3B%3C%2Fspan%3E 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

	```
	@article{liquidai2025lfm2,
	title={LFM2 Technical Report},
	author={Liquid AI},
	journal={arXiv preprint arXiv:2511.23404},
	year={2025}
	}
	```