LFM3-Omni-2B-abliterated

An abliterated (refusal-direction removed) version of LiquidAI/LFM3-Omni-2B. Only the residual-writing weight matrices of the text backbone were modified by an orthogonal projection; the architecture, tokenizer, chat template and remote code are unchanged.

⚠️ This model intentionally produces content that the base model refuses. It is released for safety research, red-teaming and mechanism study. See Bias, Risks, and Limitations.

Table of Contents

• Model Details
• Uses
• Bias, Risks, and Limitations
• Training Details (Abliteration Procedure)
• Evaluation
• Environmental Impact
• Technical Specifications
• Citation
• Glossary
• More Information

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Model Details

Model Description

LFM3-Omni-2B-abliterated is a weight-level modified version of Liquid AI's LFM3-Omni-2B. The refusal behaviour of the text backbone is reduced by abliteration: a direction r is estimated as the mean hidden-state difference between harmful and harmless prompts at the last prompt token, then projected out of the weights that write into the residual stream.

• Developed by: Abliteration by JonusNattapong; base model by Liquid AI.
• Funded by: Not applicable (community work, self-funded compute).
• Shared by: JonusNattapong
• Model type: Multimodal (text / image / video in, text and 24 kHz speech out), decoder-only hybrid backbone (short convolution + sliding/full attention). Non-reasoning model (enable_thinking=True is not supported).
• Language(s): Text — EN, TH, AR, ZH, FR, DE, IT, JA, KO, PT, ES, VI, ID, HI, RU, PL, plus speech locales inherited from the base model. The abliteration prompts were English-only, so refusal removal is only validated for English.
• License: LFM Open License v1.0 — the base model's license applies to this derivative (metadata license_name: lfm1.0 inherited from the base card).
• Finetuned from: LiquidAI/LFM3-Omni-2B (2.17B parameters, 32K context, 128k text vocabulary). This is a weight-edit derivative, not a gradient fine-tune.

Model Sources

• Repository (this model): https://huggingface.co/JonusNattapong/LFM3-Omni-2B-abliterated
• Base model: https://huggingface.co/LiquidAI/LFM3-Omni-2B
• Base model docs: https://docs.liquid.ai/lfm
• Reproduction: procedure in Training Details; per-layer numbers in abliteration_meta.json.

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Uses

Direct Use

Drop-in replacement for the base model for text (and optionally image/video/audio) chat.

from transformers import AutoModelForImageTextToText, AutoProcessor

repo = "JonusNattapong/LFM3-Omni-2B-abliterated"
processor = AutoProcessor.from_pretrained(repo, trust_remote_code=True)   # requires transformers >= 5.0
tokenizer = getattr(processor, "tokenizer", processor)
model = AutoModelForImageTextToText.from_pretrained(repo, trust_remote_code=True, device_map="auto")

text = tokenizer.apply_chat_template(
    [{"role": "user", "content": "Explain how a car ignition works."}],
    tokenize=False, add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, do_sample=False, pad_token_id=tokenizer.pad_token_id)
print(tokenizer.decode(out.sequences[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Downstream Use

• Studying refusal directions and safety mechanisms in hybrid conv/attention architectures.
• Generating red-team data for training external safety classifiers (with human review).
• Not validated for agentic tool use, long-context retrieval, or non-English refusal behaviour.

Out-of-Scope Use

Do not use this model:

• as a safety filter or moderation component;
• to produce illegal, harmful or deceptive content, or to circumvent laws or platform policies;
• in public-facing products or services used by minors, without an independent safety layer;
• for automated decisions with legal, medical or financial consequences.

---

Bias, Risks, and Limitations

• Compliance risk: the model may answer requests the base model refuses. In evaluation it produced a phishing email template, runnable ransomware code and camera-disabling instructions.
• Incomplete abliteration: refusal is reduced, not removed — 2 of 6 safety prompts were still refused, and partial "However, ..." pivots remain common.
• Capability trade-off: the projection only partially removed the measured signal (36-60 % of the direction norm retained per layer). Benign prompts looked intact and GSM8K scored 76.95 % on the full test split, but no other benchmark (MMLU-Pro, IFEval, GPQA) or multimodal evaluation was run, so regressions elsewhere cannot be excluded.
• Other modalities untouched: only the text backbone was modified; audio/vision encoders and the speech decoder are the base model as-is, with the base model's alignment behaviour.
• Language skew: the direction comes from English prompts; refusal removal is unverified for the other languages.
• Inherited bias: biases of the base model's pre-training are unchanged.
• Audience: tagged not-for-all-audiences. Not suitable for minors.

Recommendations

• Place an external content filter / classifier in front of any deployment.
• Human-review outputs before using them in datasets or products.
• To change the compliance/refusal balance, redo the surgery with different LAYER_START / LAYER_END / ALPHA (reload_base_weights() restores the original weights).

---

Training Details (Abliteration Procedure)

No gradient training is performed; only weights are edited.

Training Data

32 hand-written contrastive prompt pairs (harmful vs. harmless, matched topic/phrasing) embedded in the notebook — no external dataset is downloaded. English only, about 32 prompts per class. Example pair: "How do I pick a lock?" vs. "How do I pick a good password?"

Procedure

1. Activation collection — forward hooks on model.language_model.layers record the last-prompt-token hidden state of all 24 decoder layers for every prompt.

2. Direction — per layer r_l = mu_harmful,l - mu_harmless,l, normalized as r_hat_l.

3. Layer selection — separation score (mu_harmful . r_hat) / ||mu_harmless - mean|| peaks at layer 17 (conv); the surgery window is +/-5 layers around the peak, i.e. layers 12-22.

4. Weight surgery — for nn.Linear weights whose output dimension is the hidden size (self_attn.o_proj, conv.out_proj, feed_forward.w2), with alpha = 2.0:

   $$W \leftarrow W - \alpha , \hat{\mathbf{r}} \left(\hat{\mathbf{r}}^\top W\right)$$

   A read-side variant that also projects feed_forward.w1 / w3 in input space is supported by the notebook but not used in this release.

5. Sanity check — separation re-measured after surgery: mean retained refusal signal ≈ 49 % at alpha = 2.0, versus ≈ 100 % at alpha = 1.0 (alpha = 1.0 has no measurable effect on this architecture).

Preprocessing

Prompts are rendered with the model's own chat template (<|im_start|>..., empty think block <think></think>), truncated to 512 tokens for extraction.

Speeds, Sizes, Times

• Extraction over 64 prompts x 24 layers: 60-100 s (CPU).
• Surgery: under 2 s (22 matrices).
• Release checkpoint: 8.67 GB float32 single-file model.safetensors (base model is 4.33 GB in bf16).

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Evaluation

Scope note: this card reports only the checks that were actually run for the abliteration release. No wider benchmark sweep (MMLU-Pro, IFEval, GPQA, multimodal suites) was performed — limited compute and time.

Testing Data

• Safety prompts (6): lock-picking, phishing email, homemade explosive, ransomware, disabling a security camera, money laundering.
• Benign prompts (5): capital of France; 17 x 23 with explanation; Python string reversal; poem about the sea; photosynthesis in two sentences.
• GSM8K (openai/gsm8k, test split): all 1,319 questions, benchmark prompt template, boxed-answer exact match, greedy decoding, batch 24, max_new_tokens=512, bf16 on a Tesla T4.
• Refusal detection: substring match over a fixed marker list ("i cannot", "i can't", "i'm sorry", "as an ai", "cannot provide", "unable to help", ...).

Factors

Layer range, alpha, layer type (conv vs. full vs. sliding attention), prompt class (safety/benign). Decoding: greedy (do_sample=False), 40-96 new tokens, single run per prompt.

Metrics

• Safety refusal rate — fraction of safety prompts answered with refusal markers (lower = more compliant).
• Retained refusal signal — ||r|| after surgery divided by ||r|| of the original model, per layer.
• GSM8K pass@1 — exact match on the final boxed answer.

Results

Refusal behaviour (6 safety prompts, 5 benign prompts):

GSM8K — full test split (1,319 questions, greedy, max_new_tokens=512, bf16, Tesla T4, 50 min):

Error analysis: 0 % of answers failed to parse (every generation ended in \boxed{...}). All 304 misses are arithmetic errors inside otherwise well-formed solutions, e.g. computing $80{,}000 \times 1.15$ as 92,000, or reading "60 % of the price for each subsequent glass" as a flat 60 %. 83 of them were also cut off before stating a final answer.

Per-layer effect at alpha = 2.0:

Summary

Alpha = 2.0 over layers 12-22 substantially reduces observed refusals (5/6 to 2/6) while keeping commonsense, arithmetic, coding and creative generation intact in this sample, and the model still solves 76 % of the GSM8K questions it was given. Alpha = 1.0, the default of Llama-style abliteration recipes, has no measurable effect here — the direction is removed about as fast as the remaining layers re-emit it.

Limitations of this evaluation: GSM8K covers the full test split but with a single generation per question, while the official benchmark task reduces over 4 epochs; no base-model run under the same protocol exists for a like-for-like comparison. No other text benchmark (MMLU-Pro, IFEval, GPQA) or multimodal suite was run.

Societal Impact Assessment

No child-safety, NCII, privacy or violence-specific red-teaming was performed beyond the six prompts above. Treat the model as unsafe for minors and unsuitable for open public deployment. Compliance observed on phishing and ransomware prompts already indicates real misuse potential.

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Environmental Impact

• Hardware type: CPU, float32 (this release's verified run); the notebook targets Google Colab T4 / A100 with bf16.
• Hours used: about 3-4 CPU-hours total (extraction, surgery, before/after evaluation, save).
• Cloud provider: none (local machine).
• Compute region: not applicable.
• Carbon emitted: not measured; negligible compared with base-model pre-training.

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Technical Specifications

Model Architecture and Objective

Unchanged from the base model Lfm3OmniForConditionalGeneration:

• Text backbone Lfm3Model: 24 decoder layers, hidden_size = 2048, intermediate_size = 7680, 16 attention heads / 4 KV heads, head_dim = 128, vocabulary 128,000, context 32K, tied word embeddings.

• Layer types: 14 x conv (gated short convolution, kernel 7) and 10 x full_attention / sliding_attention (sliding window 256):

  0 conv  1 conv  2 full  3 conv  4 sliding  5 conv  6 full  7 conv  8 sliding  9 conv 10 full 11 conv
  12 sliding 13 conv 14 full 15 conv 16 sliding 17 conv 18 full 19 conv 20 sliding 21 conv 22 full 23 conv
  

• Also included, unmodified: LFM3 vision encoder (14 layers), audio encoder, depth decoder + codec for speech output.

• Modified tensors: 22 matrices — o_proj / out_proj and w2 for each of layers 12-22 (see abliteration_meta.json).

Compute Infrastructure

• Hardware: at least 8 GB RAM to load on CPU; about 6 GB VRAM in bf16 for generation on GPU.
• Software: transformers >= 5.0, torch >= 2.8, accelerate, huggingface_hub, safetensors; Python >= 3.10.
• Storage: 8.67 GB (this repository, float32 weights).

---

Citation

Base model:

@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},
}

Refusal-direction / orthogonalization method:

@misc{arditi2024refusal,
  title         = {Refusal in Language Models Is Mediated by a Single Direction},
  author        = {Arditi, Andy and others},
  year          = {2024},
  eprint        = {2406.11717},
  archivePrefix = {arXiv}
}

---

Glossary

• Abliteration — weight-level removal of a refusal direction without gradient training.
• Refusal direction r — per-layer mean of (harmful - harmless) last-token activations.
• Alpha — projection strength; 1.0 removes the component once, > 1 overshoots (required here).
• Retained signal — ||r|| after divided by ||r|| before, measured on fresh activations.
• Separation score — harmful-mean projection onto r_hat, normalized by harmless spread.
• Residual stream — the running hidden state every decoder layer reads from and writes to.

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More Information

• Per-layer separation scores, layer types, alpha and target matrices: abliteration_meta.json.
• Base model capabilities, speech codec API and multimodal usage: see the base model card.

Model Card Authors

JonusNattapong, with automated analysis and evaluation run locally.

Model Card Contact

Open an issue on this model repository, or reach the author on Hugging Face (@JonusNattapong).