---

library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE pipeline_tag: text-generation tags:

• liquid
• lfm3
• edge
• conversational
• custom_code language:
• en
• ar
• zh
• fr
• de
• it
• ja
• ko
• pt
• es
• vi
• th
• id
• hi
• ru
• pl

---

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.

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

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.