Instructions to use LiquidAI/LFM3-1.7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM3-1.7B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM3-1.7B-Base", 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-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LiquidAI/LFM3-1.7B-Base 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-Base" # 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-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LiquidAI/LFM3-1.7B-Base
- SGLang
How to use LiquidAI/LFM3-1.7B-Base 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-Base" \ --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-Base", "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-Base" \ --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-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LiquidAI/LFM3-1.7B-Base with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM3-1.7B-Base
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545fc8f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 | {
"audio_out_token": "<|audio_out|>",
"audio_token": "<|audio|>",
"backend": "tokenizers",
"bos_token": "<|startoftext|>",
"eos_token": "<|im_end|>",
"extra_special_tokens": [],
"image_token": "<|image|>",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "<|pad|>",
"response_template": {
"defaults": {
"role": "assistant"
},
"start_anchor_pattern": "<\\|im_start\\|>assistant\\n",
"fields": {
"reasoning_content": {
"open_pattern": "<think>\\n?",
"close_pattern": "</think>\\s*",
"content": "text"
},
"tool_calls": {
"open_pattern": "<tool_call>\\s*<function=(?P<name>[^\\n>]+)>",
"close_pattern": "</tool_call>\\s*",
"repeats": true,
"content": "xml-inline",
"content_args": {
"tag_pattern": "<parameter=(?P<key>[^>\\n]+)>\\n?(?P<value>.*?)\\n?</parameter>(?=\\s*(?:<parameter=|</function>\\s*\\Z|\\Z))",
"value_parser": {
"name": "text",
"args": {
"strip": false
}
}
},
"transform": {
"type": "function",
"function": {
"name": "{name}",
"arguments": "{content}"
}
}
},
"content": {
"close_pattern": "<\\|im_end\\|>\\s*",
"content": "text"
}
}
},
"tokenizer_class": "TokenizersBackend",
"video_token": "<|video|>",
"vision_end_token": "<|vision_end|>",
"vision_start_token": "<|vision_start|>"
}
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