---

library_name: gguf

license: other

license_name: lfm1.0

license_link: LICENSE

language:

• ar
• zh
• en
• fr
• de
• hi
• id
• it
• ja
• ko
• pl
• pt
• ru
• es
• th
• vi

pipeline_tag: text-generation

base_model: LiquidAI/LFM2.5-2.6B

tags:

• liquid
• lfm2.5
• gguf
• llama.cpp

---

<div align="center">

<img

src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"

alt="Liquid AI"

style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"

/>

<div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">

<a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> •

<a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> •

<a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> •

<a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>

</div>

</div>

LFM2.5-2.6B-GGUF

LFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning.

Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-2.6B

🏃 How to run LFM2

Example usage with llama.cpp:

llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation \
    --temp 0.1 --top-k 50 --repeat-penalty 1.1

QAD Q4_0 GGUF

The Quantization-Aware Distillation (QAD) checkpoint is available as

LFM2.5-2.6B-QAD-Q4_0.gguf.

This is distinct from the post-training-quantized LFM2.5-2.6B-Q4_0.gguf;

both use the GGUF Q4_0 format.

QAD source weights (safetensors)

The original FP32 QAD source checkpoint is available in

qad/, with its model config,

tokenizer, generation defaults, and the same chat template as the released QAD GGUF.

It can be loaded in Transformers by passing subfolder="qad":

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "LiquidAI/LFM2.5-2.6B-GGUF"
tokenizer = AutoTokenizer.from_pretrained(repo_id, subfolder="qad")
model = AutoModelForCausalLM.from_pretrained(
    repo_id, subfolder="qad", dtype="auto", device_map="auto"
)

inputs = tokenizer.apply_chat_template(
    [{"role": "user", "content": "What is 2 + 2?"}],
    tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

These weights are intended for fine-tuning and experimentation. Published QAD

results apply to the Q4_0 GGUF; direct FP32/BF16 inference and other quantization

formats may behave differently. See the source checkpoint documentation

for validation details and the license.