ABLITERIX TRIAL 65 BF16 LFM Open 1.0

LFM2.5-2.6B-Uncensored

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Uncensored 2.6B edge model · abliterix Trial 65 · BF16 safetensors

🌊 About this release

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:

1. Uncensored behavior edit via abliterix on ROCm (gfx1151), selecting Trial 65 LoRA and stream-merging it back to BF16.
2. Full-precision BF16 export — no quantization loss; quantized GGUFs (Q4_K_M / Q6_K / Q8_0 / IQ4_XS / IQ3_XS, imatrix-calibrated) ship in the GGUF sibling repo.

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

⚠️ Uncensored notice

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.

🧠 Model details

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

📦 What's in this repo 🚀 Usage

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

from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "SC117/LFM2.5-2.6B-Uncensored"
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [{"role": "user", "content": "What is 2+2?"}]
input_ids = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
)["input_ids"].to(model.device)
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).

🎛️ Recommended sampling

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.

🔧 Build pipeline

1. abliterix trial search on ROCm (gfx1151): 60 trials + 20 warmup, seed 117; all trials pruned only by same-prefix kl_divergence < 0.5.
2. Selected Trial 65: refusals 6/100 (baseline 90/100), KL 0.0335, length deviation 0.079 σ, generation health PASSED.
3. LoRA stream-merged into base weights in BF16 (W += B@A, alpha = r = 1).
4. BF16 GGUF converted (llama.cpp lfm2), then quantized with imatrix calibration (401 chunks from the APEX calibration set) into Q4_K_M / Q6_K / Q8_0 / IQ4_XS / IQ3_XS — see the GGUF repo.

Community derivative (behavior edit + BF16 release). Not an official Liquid AI release. Use at your own risk; follow local law and the LFM Open License v1.0.