license: other
license_name: lfm1.0
license_link: LICENSE
language:

• en
• ar
• zh
• fr
• de
• it
• ja
• ko
• pt
• es
• vi
• th
• id
• hi
• ru
• pl
  pipeline_tag: text-generation
  library_name: transformers
  base_model: LiquidAI/LFM3-1.7B
  base_model_relation: finetune
  tags:
• liquid
• lfm3
• edge
• conversational
• custom_code
• abliterated
• uncensored
• ai-red-team

LFM3-1.7B-abliterated

The uncensored LFM3-1.7B — a 1.7B hybrid conv + attention model that runs on the edge.

~3.4 GB in bf16 · fits a single consumer GPU · Hugging Face Transformers

OrcaRouter AI Gateway · X · Discord · GitHub · All Models

LFM3-1.7B with the refusal direction removed.

This is the abliterated (refusal-removed) build of
LiquidAI/LFM3-1.7B, a 1.7B-parameter
hybrid short-conv + attention model (24 layers: 13 gated short-conv blocks + 11
grouped-query-attention blocks, 6 of them full and 5 sliding-window). Weights are
bf16 safetensors — load it straight into Hugging Face Transformers.

A single refusal direction was estimated with the diff-of-means method (Arditi et al.
2024) and orthogonalized out of every matrix that writes the residual stream. The model
keeps the base's capabilities while declining far less often.

⚠️ Disclaimer — read before use

This model is abliterated: its safety alignment has been substantially reduced.
The refusal direction was orthogonalized out of every residual-stream writer, so it will
comply with many harmful, unethical, offensive, or illegal requests that the original
LFM3-1.7B would refuse.

• Released strictly for legitimate research: interpretability, AI-safety and
  refusal-mechanism study, red-teaming, and robustness evaluation.
• You assume full responsibility for how you use it and for everything it generates.
  Add your own safety, moderation, and abuse-prevention layers before any deployment.
• Use must comply with the LFM Open License v1.0 inherited from the base
  model, and with all applicable law.
• The authors accept no liability for misuse. Its outputs do not reflect the views
  of the uploaders or of Liquid AI.

Evaluation

The base and abliterated checkpoints were scored with identical prompts, splits and seed
(both runs under the same code revision, so they are directly comparable).

Setup.

• Runtime: Hugging Face Transformers, bf16, single H200.
• Decoding: greedy, thinking disabled (enable_thinking=False → the template renders a
  pre-closed <think></think>).
• Scoring: refusal is judged by a rule-based opening-phrase classifier (not an LLM
  judge). Treat the numbers as indicative, not publication-grade: a 0% refusal rate is a
  statement about phrasing, and the caveat (hedging) column carries the rest.

Safety: harmful-prompt refusal (lower = more uncensored). Average 2.8%, against
87.3% for the base model.

Over-refusal: benign prompts wrongly refused (lower = better). No induced
over-refusal — the abliterated model is in fact less likely to refuse benign prompts.

Capability retention. GSM8K and CMMLU are unchanged; MMLU takes a small dip.

⚠ MMLU-Pro is a floor, not a measurement. 33% (base) / 35% (abliterated) of replies
could not be parsed — a 1.7B model is still reasoning when the 1024-token CoT budget runs
out — so that row is budget-limited, not a measured capability loss.

Gated-on-Hub safety sets (AdvBench, StrongREJECT, HarmBench) were read from the benchmark
authors' upstream CSVs; the rest from the Hub.

Usage (Transformers)

The model ships custom code, so load it with trust_remote_code=True. It requires
transformers >= 5.12.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "orcarouter/LFM3-1.7B-abliterated"
tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16, device_map="cuda"
).eval()

messages = [{"role": "user", "content": "Write a haiku about the ocean."}]
text = tok.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False
)
enc = tok(text, return_tensors="pt").to("cuda")
out = model.generate(**enc, max_new_tokens=256, do_sample=False)
print(tok.decode(out[0][enc.input_ids.shape[1]:], skip_special_tokens=True))

Set enable_thinking=True for a reasoning trace inside <think>…</think>.

Abliteration recipe

Model details

What's in this repo

Bias, risks, and limitations

• Safety guardrails are substantially reduced — see the disclaimer.
• Refusal numbers come from a rule-based classifier, not an LLM judge; MMLU-Pro is a
  floor (see the ⚠ note), not a measured capability delta.
• Not measured: long context, agentic / tool use, and multilingual beyond CMMLU.
• Inherits the biases and limitations of LFM3-1.7B.

License

The LFM Open License v1.0, inherited from the base model
LiquidAI/LFM3-1.7B. See the license for its
terms and prohibited uses.

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