LFM2.5-2.6B is a Liquid AI 2.6B-parameter hybrid edge model built for agentic workloads: 30 layers (22 double-gated short-convolution blocks + 8 GQA), a 128K context window, 128K vocabulary, and a ChatML-like template with native <think> reasoning. It is competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks.

This release builds on the official weights in two steps:

License: LFM Open License v1.0 (same as the base model). See LICENSE.

After merging abliterix Trial 65, this model shows a much lower refusal rate and can differ substantially from official LFM2.5-2.6B. Evaluate compliance and safety for your use case; control access and audit as needed.

Implementation sketch: LoRA merge W += (B @ A) * (alpha / r) (this trial alpha = r = 1); steering applied to attn.o_proj / conv.out_proj / mlp.down_proj across 30 layers.

Coding / tool / agentic ability is largely retained; refusal and alignment behavior are changed. Official benchmark tables were not re-run for this derivative.

The lfm2 architecture is natively supported by transformers >= 5.0.0 — no trust_remote_code needed.

from transformers import AutoModelForCausalLM, AutoTokenizer
[object Object]
[object Object]

output = model.generate(
input_ids,
do_sample=True,
temperature=0.1,
top_k=50,
repetition_penalty=1.1,
max_new_tokens=512,
)
print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))

llama.cpp / LM Studio users: use the quantized GGUFs in the GGUF sibling repo (Q4_K_M / Q6_K / Q8_0 / IQ4_XS / IQ3_XS, all imatrix-calibrated).

Keep the official generation defaults: temperature 0.1, top_k 50, repetition_penalty 1.1 (they are baked into generation_config.json). If you want more creative answers, raise temperature toward 0.6–0.8; note the model always thinks before answering, so allow enough max_new_tokens for the <think> block.