Instructions to use LiquidAI/LFM2-24B-A2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2-24B-A2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM2-24B-A2B")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LiquidAI/LFM2-24B-A2B", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LiquidAI/LFM2-24B-A2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/LFM2-24B-A2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2-24B-A2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LiquidAI/LFM2-24B-A2B
- SGLang
How to use LiquidAI/LFM2-24B-A2B 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/LFM2-24B-A2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2-24B-A2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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/LFM2-24B-A2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2-24B-A2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LiquidAI/LFM2-24B-A2B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM2-24B-A2B
No other benchmarks than prefill speed and token generation speed ?
#3
by bdutta - opened
Far from being a LLM expert but I frequently see various benchmarks s.a. Aider benchmark, SWEbench, MMLU etc. that indicate quality & relevance of the generation to specific problem (or prompt) types. While the prefill speed and token generation speed are very impressive and good to see comparison against gpt-oss-20b and qwen3-30b, it'd be good to see how it compares in qualitative terms as well. Any thing planned to be shared around such aspects ?