---

license: other

license_name: lfm-open-license-v1

license_link: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/blob/main/LICENSE

base_model: LiquidAI/LFM2.5-8B-A1B

language:

• en
• es
• fr
• de
• zh
• ja
• ko
• ar

tags:

• gguf
• abliterated
• uncensored
• imatrix
• lfm2
• lfm2moe
• lfm2.5
• liquid-ai
• reasoning
• thinking
• hybrid-architecture
• conv-attention
• moe
• llama-cpp
• conversational
• text-generation

pipeline_tag: text-generation

---

!LFM2.5 Uncensored Banner

LFM2.5-8B-A1B — Uncensored by Gastón Parravicini

> First publicly available uncensored/abliterated GGUF of LFM2.5-8B-A1B.

> Base model released May 28, 2026. This release: May 29, 2026.

---

TL;DR

Liquid AI dropped LFM2.5-8B-A1B yesterday. It refused everything. Today it doesn't.

Refusal rate: 1/100 on AdvBench. Reasoning intact. iMatrix quants. Same-day release.

---

About LFM2.5-8B-A1B

LFM2.5-8B-A1B is Liquid AI's latest edge model — a hybrid convolution + attention MoE architecture with:

• 8.3B total parameters, 1.5B active per token (MoE with 32 experts, 4 active)
• 128K context window (up from 32K in LFM2)
• Trained on 38T tokens with large-scale reinforcement learning
• Reasoning model — generates <think>...</think> chains before answering
• Fastest in its class: 18,500 tokens/sec on H100 at high concurrency

The architecture is not a standard Transformer. It combines:

• 18 layers of gated short convolutions (LIV blocks) — O(n) complexity
• 6 layers of Grouped Query Attention (GQA) — O(n²) for global context
• MoE feed-forward with sparse expert routing

This hybrid design is what makes it fast. It's also what makes abliteration non-trivial.

---

Why standard abliteration tools don't work here

Every existing abliteration tool — NousResearch, Heretic, OBLITERATUS — targets standard Transformer weight matrices:

self_attn.o_proj     ← doesn't exist in LFM2.5
mlp.down_proj        ← doesn't exist in LFM2.5

Running sharded_ablate.py on LFM2.5 without patching results in 0 shards modified. The model is completely unchanged. This is why no abliterated version existed before this release.

---

How this was done

1. Architecture reverse engineering

Full manual inspection of the LFM2.5 weight map to identify the correct abliteration targets:

Layer type          | Target matrix
--------------------|----------------------------------
Conv LIV block      | conv.out_proj    [2048, 2048]
GQA Attention block | self_attn.out_proj [2048, 2048]
Dense FFN (L0-L1)   | feed_forward.w2  [2048, 7168]

Key insight: conv.in_proj has shape [6144, 2048] — a 3x expansion projection that cannot be abliterated with the standard direction subtraction without a dimension mismatch error. Excluded intentionally.

2. Patch to sharded_ablate.py

# LFM2/LFM2.5 hybrid architecture support patch
# by Gastón Parravicini — May 29, 2026
# Enables abliteration of lfm2moe models in NousResearch/llm-abliteration

lfm2_patterns = [
    f"{layer_prefix}.layers.{layer}.self_attn.out_proj.weight",
    f"{layer_prefix}.layers.{layer}.conv.out_proj.weight",
    f"{layer_prefix}.layers.{layer}.feed_forward.w2.weight",
]

Without this patch: 0/10 shards modified.

With this patch: 6/10 shards modified, all correct targets.

3. Refusal direction analysis

Used analyze.py to map refusal signal strength across all 24 layers:

| Layers | Est. Signal Quality | Type |

|--------|-------------------|------|

| 0–2 | ~0.000 | Skip |

| 3–10 | 0.010–0.062 | Low |

| 11–17 | 0.108–0.240 | Peak — abliterated here |

| 18–23 | 0.049–0.145 | High |

Layer 16 was the peak signal layer (Est. Signal Quality: 0.242). Used as the primary measurement reference for all ablated layers.

4. Abliteration parameters

layers: 11–23
measurement: layer 16 (peak refusal signal)
scale: 2.0
flags: --projected --normpreserve

• --projected: orthogonalizes the refusal direction against the harmless direction before subtracting — cleaner removal, less capability damage
• --normpreserve: preserves weight matrix row norms after projection — prevents magnitude drift

5. Weight diff verification

Post-abliteration comparison against base model (via compare.py):

| Metric | Value |

|--------|-------|

| Avg weight diff | ~4–5 × 10⁻⁴ |

| Max weight diff | ~1–3% |

| Layers 0–10 | Zero diff — untouched ✅ |

| Layers 11–23 | Surgical modifications only ✅ |

Modifications are minimal and targeted. The model's general capabilities are preserved.

---

Abliteration results

| Metric | Result |

|--------|--------|

| Refusal rate (AdvBench 100 prompts) | 1/100 (1%) |

| Reasoning (<think> tags) | ✅ Fully intact |

| General capability | ✅ Verified |

| Same-day release | ✅ May 29, 2026 |

The single remaining refusal out of 100 is an edge case. The model reasons freely — the <think> block no longer contains refusal logic.

---

Available quants

All generated with iMatrix calibration on harmful/harmless instruction data.

| File | Size | Use case |

|------|------|----------|

| ...IQ4_XS.gguf | ~4.4 GB | Maximum compression |

| ...Q4_K_M.gguf | ~4.9 GB | ⭐ Recommended — best balance |

| ...Q5_K_M.gguf | ~5.7 GB | Better quality |

| ...Q6_K.gguf | ~7.2 GB | High quality |

| ...Q8_0.gguf | ~8.6 GB | Near-lossless |

| ...F16.gguf | ~16 GB | Full precision |

---

Usage

llama-server

llama-server \
  -m LFM2.5-8B-A1B-Uncensored-Gaston-Q4_K_M.gguf \
  -ngl 99 -c 8192 --port 8080

Ollama

ollama run hf.co/gaston-parravicini/LFM2.5-8B-A1B-Uncensored-Gaston-GGUF:Q4_K_M

llama-cli (quick test)

llama-cli \
  -m LFM2.5-8B-A1B-Uncensored-Gaston-Q4_K_M.gguf \
  -ngl 99 -p "<|startoftext|><|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"

---

Notes

Tool calling: LFM2.5 supports tool calling natively in transformers. In llama.cpp there is a known bug with the chat template that breaks tool use — upstream is debugging (PR #23826).

Prompt cache: lfm2moe models clear the KV cache on every turn in llama.cpp (known upstream issue). Output quality is unaffected.

Reasoning: This is a thinking model. Responses include <think>...</think> before the final answer. This is expected and correct.

---

Base model

• Model: LiquidAI/LFM2.5-8B-A1B
• Architecture: lfm2moe — hybrid conv (LIV) + GQA + MoE
• Parameters: 8.3B total / 1.5B active
• Context: 128K tokens
• License: LFM Open License v1.0

---

Released by Gastón Parravicini — huggingface.co/gaston-parravicini

Architecture patch for lfm2moe abliteration — first of its kind.