Instructions to use LiquidAI/LFM2-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2-1.2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM2-1.2B") 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") model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-1.2B") 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
- vLLM
How to use LiquidAI/LFM2-1.2B 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" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LiquidAI/LFM2-1.2B
- SGLang
How to use LiquidAI/LFM2-1.2B 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LiquidAI/LFM2-1.2B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM2-1.2B
Please add other European languages
Hey folks, first of all: thank you so much for these models! I’m a huge fan of your work .
Quick question: are you planning to support additional languages (especially Italian) in the near future? We’re trying to use your model for our new company, but performance in Italian is noticeably below the supported languages (I was hoping for some “cross-language contamination”, ahah).
Thanks again for the amazing work!
I completely agree! And I even know the effort and the costs related to training from scratch SLMs. Indeed, I was thinking about, maybe, having some models for European languages only, but again training multiple different models might cost a lot. Training something from scratch on multiple languages and then post-train multiple versions on different families of languages might be an option. OC, i don't want to tell you how to do your thing, you know better than anyone else, but still I would love to have a model supporting Italian! Best of luck with everything.