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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-1.2B-RAG")
model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-1.2B-RAG", device_map="auto")
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=256)
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-RAG 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-RAG"
# 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-RAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

mlabonne commited on Sep 25, 2025

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@@ -121,16 +121,6 @@ You can automatically apply it using the dedicated [`.apply_chat_template()`](ht
 RAG systems enable AI solutions to include new, up-to-date, and potentially proprietary information in LLM responses that was not present in the training data. When a user asks a question, the retrieval component locates and delivers related documents from a knowledge base, and then the RAG generator model answers the question based on facts from those contextual documents.
-## 📈 Performance
-We evaluated the model across 3 metrics using LLMs as a judge, comparing against 4 similarly-sized open-source models:
-- **Groundedness**: Do the model’s responses consist entirely of information from the provided contextual documents and avoid hallucinations?
-- **Relevance**: Does the model answer the user’s question concisely? Does all of the response content contribute to the final answer without inclusion of unnecessary fluff?
-- **Helpfulness**: Overall, how well did the model assist with the user’s query?
-LFM2-1.2B-RAG achieves competitive performance in all 3 metrics compared to Qwen3-1.7B, Gemma3-1B-IT, Llama-3.21B-Instruct, and Pleias-1B-RAG.
 ## 🏃 How to run
 - Hugging Face: [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)

 RAG systems enable AI solutions to include new, up-to-date, and potentially proprietary information in LLM responses that was not present in the training data. When a user asks a question, the retrieval component locates and delivers related documents from a knowledge base, and then the RAG generator model answers the question based on facts from those contextual documents.
 ## 🏃 How to run
 - Hugging Face: [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B)