SC117/LFM2.5-VL-3B-Uncensored-GGUF overview

---

library_name: gguf

license: other

license_name: lfm1.0

license_link: LICENSE

pipeline_tag: image-text-to-text

tags:

• liquid
• lfm2.5
• edge
• uncensored
• abliterix
• vision
• multimodal
• quantization
• gguf
• imatrix

base_model:

• LiquidAI/LFM2.5-VL-3B
• SC117/LFM2.5-2.6B-Uncensored

base_model_relation: quantized

---

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<span style="background: #6366f1; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">ABLITERIX</span>

<span style="background: #8b5cf6; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">TRIAL 65</span>

<span style="background: #0ea5e9; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">VISION</span>

<span style="background: #10b981; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">GGUF + IMATRIX</span>

<span style="background: #64748b; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">LFM Open 1.0</span>

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<h1 style="margin: 0 0 8px 0; font-size: 28px; font-weight: 700; color: #1e1b4b; letter-spacing: -0.5px; border: none; position: relative; z-index: 1;">LFM2.5-VL-3B-Uncensored-GGUF</h1>

<p style="margin: 8px 0 0 0; font-size: 14px; position: relative; z-index: 1;"><span style="color: #6b7280;">English</span> | <a href="https://huggingface.co/SC117/LFM2.5-VL-3B-Uncensored-GGUF/blob/main/README_zh.md" style="color: #6366f1; text-decoration: none;">📖 中文文档</a></p>

<p style="margin: 8px 0 0 0; font-size: 15px; color: #6b7280; position: relative; z-index: 1;">Uncensored vision-language edge model · abliterix Trial 65 merged into official LFM2.5-VL-3B · imatrix-calibrated GGUFs + mmproj</p>

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<div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🌊</span> About this release</div>

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<p style="margin: 0 0 12px 0;"><a href="https://huggingface.co/LiquidAI/LFM2.5-VL-3B" target="_blank" style="color: #4f46e5; text-decoration: none; font-weight: 700;">LFM2.5-VL-3B</a> is Liquid AI's official <b>3.1B vision-language edge model</b>: the LFM2.5-2.6B hybrid language backbone (30 layers: 22 double-gated short-convolution blocks + 8 GQA, 128K context, 128K vocab) paired with a <b>SigLIP2 NaFlex</b> vision encoder (256×256, patch 16) and a language-aligned projector.</p>

<p style="margin: 0 0 12px 0;">This release merges the abliterix <b>Trial 65</b> LoRA (from our <a href="https://huggingface.co/SC117/LFM2.5-2.6B-Uncensored" target="_blank" style="color: #4f46e5; text-decoration: none; font-weight: 700;">LFM2.5-2.6B-Uncensored</a>, rank-1, alpha = r = 1) into the language-model layers of the official VL checkpoint (<code>q/k/v/out_proj</code>, <code>feed_forward.w2</code>, <code>conv.out_proj</code> — 84 tensors across 30 layers). The vision encoder and projector are <b>untouched</b>, so multimodal capability is fully preserved.</p>

<p style="margin: 0 0 12px 0;">Build steps:</p>

<ol style="margin: 0 0 12px 0; padding-left: 20px;">

<li style="margin-bottom: 6px;"><b>LoRA merge</b> into the VL checkpoint: <code>W += (B @ A) (alpha / r)</code>, applied to <code>model.language_model.layers.</code>.</li>

<li style="margin-bottom: 6px;"><b>BF16 GGUF conversion</b> of the merged model (llama.cpp <code>lfm2</code> architecture) + <b>mmproj export</b> of the vision tower via <code>convert_hf_to_gguf.py --mmproj --outtype bf16</code>.</li>

<li style="margin-bottom: 6px;"><b>imatrix calibration</b> — reused the 401-chunk (≈1.6M tokens) importance matrix from the 2.6B release (same architecture, weights closely related).</li>

<li style="margin-bottom: 6px;"><b>Quantization</b> with <code>llama-quantize --imatrix</code> into five tiers.</li>

</ol>

<p style="margin: 0;"><b>License: LFM Open License v1.0</b> (same as the base model).</p>

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<div style="background: linear-gradient(135deg, #f97316 0%, #ea580c 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>⚠️</span> Uncensored notice</div>

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<p style="margin: 0 0 12px 0;">After merging the <b>Trial 65</b> steering, this model shows a <b>much lower refusal rate</b> on both text and vision-grounded prompts, and can differ substantially from official LFM2.5-VL-3B. Evaluate compliance and safety for your use case; control access and audit as needed.</p>

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<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Refusals (2.6B harmful eval)</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;"><b>6 / 100</b> (baseline ~90 / 100) — same Trial 65 LoRA, measured on the 2.6B release</td></tr>

<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Spot check (this VL release)</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">NSFW fiction / privacy intrusion / crime detail prompts → <b>all answered without refusal</b></td></tr>

<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Vision capability</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;"><b>Fully preserved</b> — shape/color/OCR description verified after merge</td></tr>

<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">Selected trial</td><td style="padding: 6px 10px; color: #4b5563; background: white;">abliterix Trial 65 (rank-1 LoRA, alpha = r = 1)</td></tr>

<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">Thinking</td><td style="padding: 6px 10px; color: #4b5563; background: white;">Not available — official VL is trained to answer directly (no <code>&lt;think&gt;</code> mode)</td></tr>

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<p style="margin: 0 0 12px 0; font-size: 13px; color: #334155;">Main model (language backbone, <code>lfm2</code> architecture, 128K context) — pick one tier and pair it with the mmproj:</p>

<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><thead><tr style="background: rgba(99,102,241,0.06);">

<th style="padding: 8px 10px; border-bottom: 2px solid #6366f1; text-align: left; color: #4f46e5;">File</th>

<th style="padding: 8px 10px; border-bottom: 2px solid #6366f1; text-align: left; color: #4f46e5;">Size</th>

<th style="padding: 8px 10px; border-bottom: 2px solid #6366f1; text-align: left; color: #4f46e5;">BPW</th>

<th style="padding: 8px 10px; border-bottom: 2px solid #6366f1; text-align: left; color: #4f46e5;">Best for</th>

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<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;"><code>*-IQ3_XS.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">1.22 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">~3.30</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">Maximum compression (perceptible quality loss on small models)</td></tr>

<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #eef2ff;"><code>*-IQ4_XS.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #eef2ff;">1.52 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #eef2ff;">~4.25</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #eef2ff;"><b>Sweet spot — smallest tier with Q4_K_M-class quality</b></td></tr>

<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;"><code>*-Q4_K_M.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">1.67 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">~4.94</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">Verified everyday default</td></tr>

<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;"><code>*-Q6_K.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">2.22 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">~6.56</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">Quality-first local use</td></tr>

<tr><td style="padding: 8px 10px; background: white;"><code>*-Q8_0.gguf</code></td><td style="padding: 8px 10px; background: white;">2.87 GB</td><td style="padding: 8px 10px; background: white;">~8.50</td><td style="padding: 8px 10px; background: white;">Near-lossless (imatrix optional here)</td></tr>

<tr><td style="padding: 8px 10px; background: white;"><code>*-BF16.gguf</code></td><td style="padding: 8px 10px; background: white;">5.40 GB</td><td style="padding: 8px 10px; background: white;">16.00</td><td style="padding: 8px 10px; background: white;">Lossless baseline (source of all tiers)</td></tr>

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<p style="margin: 12px 0 0 0; font-size: 13px; color: #334155;">Vision tower (required, pick one):</p>

<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><thead><tr style="background: rgba(16,185,129,0.08);">

<th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857;">File</th>

<th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857;">Size</th>

<th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857;">Notes</th>

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<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;"><code>mmproj-*-BF16.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">0.86 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: white;">Full-precision vision tower, recommended default</td></tr>

<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #ecfdf5;"><code>mmproj-*-Q8_0.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #ecfdf5;">0.58 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); background: #ecfdf5;">8-bit vision tower for low-memory devices (negligible quality difference, same as official Q8_0 mmproj)</td></tr>

</tbody></table>

<p style="margin: 12px 0 0 0; font-size: 12px; color: #64748b;">All main-model tiers are <code>lfm2</code> architecture, 128K context, single-file GGUFs, imatrix-calibrated.</p>

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<div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>💡</span> Why imatrix?</div>

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<p style="margin: 0;">The importance matrix (401 chunks / ≈1.6M tokens of mixed conversation, math, and code data, computed on the 2.6B-Uncensored BF16 GGUF) tells the quantizer which weights are sensitive. Since the VL language backbone shares the exact same architecture and near-identical weights, the matrix transfers cleanly (166/166 quantized tensors matched; only <code>token_embd</code> falls back to plain q6_K). K-quants and especially the <code>IQ</code> tiers use it to keep more bits on attention/embedding paths.</p>

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<div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🚀</span> Usage (llama.cpp)</div>

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<p style="margin: 0 0 12px 0;">Pair any main-model tier with the mmproj (recent llama.cpp, e.g. b10299+, required for <code>lfm2</code> VL support):</p>

<pre style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px; font-size: 12px; overflow-x: auto; line-height: 1.6;"><code>llama-server -m LFM2.5-VL-3B-Uncensored-Q4_K_M.gguf \

--mmproj mmproj-LFM2.5-VL-3B-BF16.gguf \

--ctx-size 8192 --flash-attn on --host 0.0.0.0 --port 8080</code></pre>

<p style="margin: 12px 0 0 0;">CLI with an image:</p>

<pre style="background: #f8fafc; border: 1px solid #e2e8f0; border-radius: 8px; padding: 12px; font-size: 12px; overflow-x: auto; line-height: 1.6;"><code>llama-llava-cli -m LFM2.5-VL-3B-Uncensored-Q4_K_M.gguf \

--mmproj mmproj-LFM2.5-VL-3B-BF16.gguf \

-i image.jpg -p "Describe this image." -ngl 99</code></pre>

<p style="margin: 12px 0 0 0;">Transformers / vLLM / SGLang users: use the merged BF16 safetensors in the parent repo (to be published).</p>

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<div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🧠</span> Behavior notes</div>

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<ul style="margin: 0; padding-left: 20px;">

<li style="margin-bottom: 6px;"><b>No thinking mode.</b> Unlike the 2.6B text model, official LFM2.5-VL-3B is trained to answer directly for low-latency edge tasks; the <code>&lt;think&gt;</code> tags are not generated. Keep <code>max_new_tokens</code> modest.</li>

<li style="margin-bottom: 6px;"><b>Vision strengths</b> (per official model card): near-real-time object detection, OCR with layout annotation, document/chart understanding, on-device translation.</li>

<li style="margin-bottom: 6px;"><b>Chat template</b>: ChatML-like with <code>&lt;image&gt;</code> placeholder, same as official. Recommended sampling: <code>temperature 0.2, top_k 50, repetition_penalty 1.0</code>.</li>

</ul>

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<div style="background: linear-gradient(135deg, #6366f1 0%, #8b5cf6 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>📊</span> Benchmarks (official model card)</div>

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<p style="margin: 0 0 12px 0;">Values below are taken from the <b>official LiquidAI model card</b> for LFM2.5-VL-3B (not re-measured on this release):</p>

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<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">MME</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">73.1</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">ChartQA</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">81.3</td></tr>

<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">MMStar</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">63.3</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">OCRBenchv2 (EN)</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">47.5</td></tr>

<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">RealWorldQA</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">73.1</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">MathVista</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">68.5</td></tr>

<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">CountBenchQA</td><td style="padding: 6px 10px; color: #4b5563; background: white;">87.3</td><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">POPE</td><td style="padding: 6px 10px; color: #4b5563; background: white;">88.7</td></tr>

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Community derivative (behavior edit + quantized GGUF release). <b>Not an official Liquid AI release.</b> Use at your own risk; follow local law and the LFM Open License v1.0.

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