Instructions to use LiquidAI/LFM3-1.7B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

API Try Docs Blog

LFM3-1.7B-Base

LFM3-1.7B-Base is the pre-trained checkpoint behind LFM3-1.7B, a text model for local agents and specialized applications based on our new tri-hybrid architecture.

Pre-trained on 22T tokens: the last 3.5T tokens are curated mid-training data with agentic traces and tool calls, and extend the context window to 256K tokens.
Designed for the edge: at 4-bit precision, it takes about 1 GB of memory, so it fits on any phone or laptop.
Made to be specialized: a starting point for continued pre-training and your own post-training.

Find more information about the LFM3 family in our release blog post.

Model details

The LFM3 family includes base, text, vision, audio, omni, and decision models.

Property	Specification
Checkpoint	Pre-trained, text-only base model
Parameters	1.73B
Architecture	24 layers: 13 gated short convolutions, 5 sliding-window attention, 6 global attention
Context length	256K tokens (input + output)
Vocabulary	128,000 tokens
Languages	English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
License	LFM Open License v1.0

LFM3-1.7B-Base is the checkpoint used to create LFM3-1.7B. It is only recommended for tasks that require heavy fine-tuning, like language-specific or domain-specific assistants, training on proprietary data, or experimenting with novel post-training approaches. For chat, tool calling, and agents, use LFM3-1.7B.

Inference

LFM3-1.7B-Base uses the same architecture and modeling code as LFM3-1.7B, with BF16 weights.

Transformers quick start:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "LiquidAI/LFM3-1.7B-Base"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
    trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)

prompt = "The nematode C. elegans is"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(model.device)
output = model.generate(input_ids, max_new_tokens=128, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Mid-training included chat data with and without thinking traces, formatted with the same chat template as LFM3-1.7B. With apply_chat_template, reasoning is enabled by default, so responses start with a thinking trace. Set enable_thinking=False to prefill an empty thinking trace and have the model answer directly.

Fine-tuning

LFM3-1.7B-Base is the starting point for substantial customization, such as adapting to new domains, languages, or behaviors that diverge significantly from those of LFM3-1.7B, through continued pre-training or full post-training via TRL.

These notebooks start from LFM3-1.7B. To start from the base model, set model_id = "LiquidAI/LFM3-1.7B-Base".

Training	Use case	Notebook
SFT	Extract structured JSON from messy documents
CPT	Learn a new language (Turkish) from raw text

Training

Pre-training: two stages build LFM3-1.7B-Base on 22T tokens in total.

Main pre-training: compared with LFM2, the mix adds more scientific papers, repository-level code, math, and a wider range of knowledge sources.
Mid-training: the last 3.5T tokens use curated, higher-quality data with a larger share of agentic traces and tool calls. This stage also extends the context window to 256K tokens.

Post-training from this checkpoint (SFT, mixed RL, multi-domain on-policy distillation, and agentic RL) produces LFM3-1.7B.

Citation

@article{liquidAI2026LFM3,
  author  = {Liquid AI},
  title   = {Introducing LFM3: Every Modality for Specialized Intelligence},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm3},
}

For questions and feedback, join Discord. For custom deployment and fine-tuning support, contact Liquid AI.