Instructions to use LiquidAI/LFM3-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM3-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM3-1.7B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM3-1.7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LiquidAI/LFM3-1.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/LFM3-1.7B" # 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/LFM3-1.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LiquidAI/LFM3-1.7B
- SGLang
How to use LiquidAI/LFM3-1.7B 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/LFM3-1.7B" \ --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/LFM3-1.7B", "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/LFM3-1.7B" \ --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/LFM3-1.7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LiquidAI/LFM3-1.7B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM3-1.7B
Download config.json from LiquidAI/LFM3-1.7B: direct link, hf CLI and curl.
- Browser
- Download file 1.45 kB
-
https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/config.json
- Command line
-
hf download hf://LiquidAI/LFM3-1.7B/config.json
-
curl -L -o config.json https://huggingface.co/LiquidAI/LFM3-1.7B/resolve/main/config.json
1.45 kB
| { | |
| "architectures": [ | |
| "Lfm3ForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_lfm3.Lfm3Config", | |
| "AutoModel": "modeling_lfm3.Lfm3Model", | |
| "AutoModelForCausalLM": "modeling_lfm3.Lfm3ForCausalLM" | |
| }, | |
| "bos_token_id": 124894, | |
| "conv_L_cache": 7, | |
| "conv_bias": false, | |
| "dtype": "bfloat16", | |
| "eos_token_id": 124900, | |
| "head_dim": 128, | |
| "hidden_size": 2048, | |
| "intermediate_size": 7680, | |
| "layer_types": [ | |
| "conv", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "sliding_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "sliding_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "sliding_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "sliding_attention", | |
| "conv", | |
| "full_attention", | |
| "conv", | |
| "sliding_attention", | |
| "conv", | |
| "full_attention", | |
| "conv" | |
| ], | |
| "max_position_embeddings": 262144, | |
| "model_type": "lfm3", | |
| "norm_eps": 1e-05, | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "num_key_value_heads": 4, | |
| "pad_token_id": 124893, | |
| "rope_parameters": { | |
| "full_attention": { | |
| "partial_rotary_factor": 0.5, | |
| "rope_theta": 1000000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_attention": { | |
| "rope_theta": 10000, | |
| "rope_type": "default" | |
| } | |
| }, | |
| "sliding_window": 256, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.12.1", | |
| "use_cache": true, | |
| "use_sandwich_norm": true, | |
| "vocab_size": 128000 | |
| } | |