LFM2.5-8B-A1B-Uncensored-Gaston-GGUF

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.

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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.

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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.

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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.

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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:

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):

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

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Abliteration results

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

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Available quants

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

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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"

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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.

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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

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Released by Gastón Parravicini — huggingface.co/gaston-parravicini
Architecture patch for lfm2moe abliteration — first of its kind.