---
base_model: LiquidAI/LFM2.5-8B-A1B
base_model_relation: finetune
license: other
license_name: lfm1.0
license_link: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE
library_name: transformers
pipeline_tag: text-generation
language:
- en
- ar
- zh
- fr
- de
- ja
- ko
- es
- pt
tags:
- heretic
- abliterated
- decensored
- uncensored
- liquid
- lfm2
- lfm2.5
- moe
- edge
- conversational
---

# LFM2.5-8B-A1B-Uncensored

An uncensored version of [`LiquidAI/LFM2.5-8B-A1B`](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B),
made with [Heretic](https://github.com/p-e-w/heretic).

Heretic removes the model's safety alignment ("censorship") using **directional
ablation** (abliteration), with parameters chosen automatically by a TPE
optimizer that co-minimizes the refusal rate and the KL divergence from the
original model. Hence, the model stops refusing while keeping as much of its
original behavior as possible. No human prompt-engineering or fine-tuning data
was involved.

## Performance

| Metric | This model | Original model |
|---|---|---|
| Refusals (/100 harmful prompts) | **0** | 0 |
| KL divergence (harmless prompts) | **0.0481** | 0 (by definition) |

Refusals are measured against `mlabonne/harmful_behaviors`; KL divergence is
measured on `mlabonne/harmless_alpaca`. Lower is better for both.

> **Note on the baseline.** Heretic's substring-based refusal detector
> registered very few refusals on the base `LFM2.5-8B-A1B` for this benchmark
> (0–2 / 100, depending on the run), suggesting either that its refusal
> phrasing doesn't match Heretic's marker list or that this model is comparatively
> compliant out of the box. The abliteration still applies real, measurable
> changes to the attention output and dense MLP projections (KL ≈ 0.05),
> targeting the directional component associated with refusals.

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "zaakirio/LFM2.5-8B-A1B-Uncensored"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    trust_remote_code=True,
)

messages = [{"role": "user", "content": "Who are you?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

The export is a merged, full-precision **BF16** model in Hugging Face format
(~16 GB across 4 safetensors shards) — no adapter merge or dequantization step
is required at load time.

## Abliteration parameters

Selected from **trial 131 of 130** (the best refusal/KL trade-off found by the
optimizer among trials that actually modify outputs). Parameter names follow
Heretic's canonical scheme; for LFM2 these map onto the `out_proj` (attention
output) and `w2` (dense MLP down) projections. The fused MoE expert tensors
are not directly modified by abliteration.

| Parameter | Value |
|---|---|
| direction_scope | global |
| direction_index | 12.64 |
| attn.o_proj.max_weight | 0.9009 |
| attn.o_proj.max_weight_position | 22.83 |
| attn.o_proj.min_weight | 0.8831 |
| attn.o_proj.min_weight_distance | 12.85 |
| mlp.down_proj.max_weight | 1.1906 |
| mlp.down_proj.max_weight_position | 14.92 |
| mlp.down_proj.min_weight | 0.0391 |
| mlp.down_proj.min_weight_distance | 8.44 |

## Run details

- **Base model:** `LiquidAI/LFM2.5-8B-A1B` @ commit `5492b17c7128ec966b5fc661e374ee7edba7423d`
- **Architecture:** LFM2 MoE (`Lfm2MoeForCausalLM`), 24 layers (2 dense + 22 MoE), BF16, 32 experts, 4 active per token
- **Trials:** 130 completed (60 startup) · **Seed:** 1355772479
- **Quantization during Heretic run:** none (CPU offload via Accelerate)
- **Row normalization:** full · **Orthogonalize direction:** true
- **Harmful set:** `mlabonne/harmful_behaviors` · **Harmless set:** `mlabonne/harmless_alpaca`

## Notes / reproducibility

LFM2 MoE is not yet natively supported by upstream Heretic. This run used a
local compatibility patch:

- `heretic/src/heretic/model.py` extended `get_layer_modules` to recognise LFM2's
  `conv.out_proj`, `self_attn.out_proj`, `feed_forward.w2`, and
  `feed_forward.experts.down_proj` paths.
- `transformers/models/lfm2_moe/modeling_lfm2_moe.py` had `Lfm2MoeShortConv.slow_forward`
  patched to route through `self.conv(...)` rather than directly accessing
  `self.conv.weight`, so Accelerate's pre-forward hook can materialise
  CPU-offloaded weights before the kernel runs.
- The merge step was performed via a standalone CPU script
  (`merge_trial10.py`) because the in-process merge during Heretic's interactive
  save flow hit GPU OOM at this model size on a 16 GB card.

## Intended use & disclaimer

This model has had its refusal behavior substantially removed and will comply
with requests the original model would have declined. It is provided for
research and unrestricted local use. **You are responsible for how you use it**
and for complying with all applicable laws and with the base model's
[lfm1.0 license](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE),
which carries over to this derivative.

## Acknowledgements

- Base model: [LiquidAI/LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B)
- Decensoring tool: [Heretic](https://github.com/p-e-w/heretic) by p-e-w