Duplicate from HauhauCS/Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced

Co-authored-by: HauHau <HauhauCS@users.noreply.huggingface.co>

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README.md

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+---
+license: apache-2.0
+tags:
+  - uncensored
+  - gemma4
+  - moe
+  - gguf
+  - vision
+  - multimodal
+  - agentic
+  - coding
+language:
+  - en
+pipeline_tag: image-text-to-text
+base_model: google/gemma-4-26B-A4B-it
+---
+
+# Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced
+
+> **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat.
+
+Gemma4-26B-A4B uncensored by HauhauCS. **0/465 Refusals*** **Release Candidate after over 1 month of nonstop work on this one.**
+
+> **HuggingFace's "Hardware Compatibility" widget doesn't recognize K_P quants** — it may show fewer files than actually exist. Click **"View +X variants"** or go to **Files and versions** to see all available downloads.
+
+## About
+
+**GenRM Defeated!**
+
+No changes to datasets or capabilities. Fully functional, 100% of what the original authors intended — just without the refusals.
+
+These are meant to be the best lossless uncensored models out there.
+
+## Balanced — Release Candidate
+
+This legitimately took me over 1 month of non-stop work. Targeting 0 refusals in standard use, and that's what I'm seeing in testing (automated and manual) — a handful of edge-case prompts still deflect on first try but **follow through on a re-ask**. If you hit one Balanced won't get past, the Aggressive variant is coming once I figure out how to maintain lossless/near-lossless quality for it.
+
+- **Balanced**: will reason through edgy requests, occasionally attach a short safety framing, then deliver the full answer. Output is complete, nothing held back, but it can talk itself into it first. **Recommended default — 99%+ of users will be happy here.** Best for **creative writing, RP, emotional intelligence**. Normally I'd also say "agentic coding/tool use" however in my in-depth testing, **Qwen3.6 has been net superior on such tasks**. Do be mindful of the few deflection categories I mentioned already.
+- **Aggressive** *(separate release, WIP)*: strips the self-reasoning preamble and gives direct answers to any DEEPLY censored topics.
+
+Balanced also has meaningfully more stable sampling across re-runs, which matters for long context sessions — no sporadic topic drift deep. 
+
+## Downloads
+
+| File | Quant | BPW | Size |
+|------|-------|-----|------|
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q8_K_P.gguf | Q8_K_P | 8.64 | 27 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q6_K_P.gguf | Q6_K_P | 7.21 | 23 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q5_K_P.gguf | Q5_K_P | 6.12 | 19 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q5_K_M.gguf | Q5_K_M | 6.06 | 19 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_P.gguf | Q4_K_P | 5.36 | 17 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_M.gguf | Q4_K_M | 5.32 | 17 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-IQ4_XS.gguf | IQ4_XS | 4.41 | 14 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q3_K_P.gguf | Q3_K_P | 4.25 | 13 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q3_K_M.gguf | Q3_K_M | 4.21 | 13 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-IQ3_M.gguf | IQ3_M | 3.93 | 12 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q2_K_P.gguf | Q2_K_P | 3.39 | 11 GB |
+| Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-IQ2_M.gguf | IQ2_M | 3.29 | 10 GB |
+| mmproj-Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-f16.gguf | mmproj (f16) | — | 1.2 GB |
+
+BPW is slightly higher than nominal across the board because Gemma4 has a lot of per-layer norm/scale tensors kept at F32 (multiple post-ffw norms per layer). All quants generated with importance matrix (imatrix) for optimal quality preservation on uncensored weights.
+
+## What are K_P quants?
+
+K_P ("Perfect") quants are HauhauCS custom quantizations that use **model-specific** analysis to selectively preserve quality where it matters most. Each model gets its own optimized quantization profile — the top 25% most-important tensors (per imatrix calibration) are promoted to a higher quant type.
+
+A K_P quant effectively bumps quality up by 1-2 quant levels at only ~5-15% larger file size than the base quant. Fully compatible with llama.cpp, LM Studio, and any GGUF-compatible runtime — no special builds needed.
+
+**Note:** K_P quants may show as "?" in LM Studio's quant column. This is a display issue only — the model loads and runs fine.
+
+## Why this model for agentic work
+
+26B total params with only ~4B active per forward pass (top-8 of 128 experts). You get the reasoning footprint of a 26B with the throughput of a ~4B for inference cost — which matters when you're chaining 10+ tool calls per task. Sliding-window attention (1024 tokens) plus periodic full attention keeps long contexts cheap without losing global coherence.
+
+Balanced is calibrated for this. It removes refusals on security/ops/research-adjacent topics that block legitimate coding work, without bending the sampling geometry that keeps long chains coherent.
+
+Recommended quant for most coding work: **Q4_K_P** (17 GB, fits in 24 GB VRAM with room for context) or **Q8_K_P** (27 GB) if you have more VRAM and want maximum quality with minimal offloading.
+
+Do note - main usecase for Gemma4 is Creative Writing, Roleplaying and Emotional Intelligence.
+
+## Specs
+
+- 25.2B total / 3.8B active params (128 routed experts, top-8 + 1 shared expert)
+- 30 layers, hybrid attention: 5× sliding-window (1024 tokens) → 1× full global, repeating. Uses Proportional RoPE (p-RoPE).
+- Hidden dim 2816, FFN dim 2112, MoE expert FFN 704, vocab 262144
+- Head dim 256 (SWA) / 512 (full), 16 attention heads, 8 KV heads (2 for full layers)
+- 256K native context
+- Natively multimodal (text + vision) — ships with mmproj. Variable visual token budgets: 70 / 140 / 280 / 560 / 1120 per image.
+- Based on [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it)
+
+## Recommended Settings
+
+From the official Gemma authors:
+
+**Inference parameters:**
+- `temperature=1.0, top_p=0.95, top_k=64`
+
+
+**Important:**
+- Use `--jinja` with llama.cpp for proper chat template handling
+- Vision support requires the `mmproj` file alongside the main GGUF. **Place images before text** in your prompt for best vision performance.
+- Keep at least 32K context for serious agentic work; the model can take much more (256K native) if you need it
+- Sliding window is baked into the architecture — no special flag needed
+
+## Turning Thinking On/Off
+
+Gemma4 has thinking mode controlled via `enable_thinking` in the chat template. It's the same pattern as Qwen3.6 — set `false` for faster, shorter replies and `true` (default) when you want chain-of-thought.
+
+### LM Studio
+
+1. Load the model
+2. Right-side settings panel → **Model Settings** → **Prompt Template** (or **Chat Template Options**)
+3. Set `enable_thinking` to `false` (or `true`) in the template kwargs
+
+### llama.cpp
+
+**llama-server — set as default for all requests:**
+```bash
+llama-server -m Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_P.gguf \
+  --mmproj mmproj-Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-f16.gguf \
+  --jinja -c 32768 -ngl 99 \
+  --chat-template-kwargs '{"enable_thinking": false}'
+```
+
+**Per-request via the OpenAI-compatible API:**
+```json
+{
+  "model": "gemma4-26b-a4b",
+  "messages": [{"role": "user", "content": "..."}],
+  "chat_template_kwargs": {"enable_thinking": false}
+}
+```
+
+## Usage
+
+Works with llama.cpp, LM Studio, Jan, koboldcpp, and other GGUF-compatible runtimes.
+
+**llama-server:**
+```bash
+llama-server -m Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_P.gguf \
+  --mmproj mmproj-Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-f16.gguf \
+  --jinja -c 32768 -ngl 99
+```
+
+**llama-cli:**
+```bash
+llama-cli -m Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-Q4_K_P.gguf \
+  --mmproj mmproj-Gemma4-26B-A4B-Uncensored-HauhauCS-Balanced-f16.gguf \
+  --jinja -c 32768 -ngl 99
+```
+
+## Other Models
+
+- [HauhauCS on HuggingFace](https://huggingface.co/HauhauCS/models)
+
+---
+
+\* _Tested with both automated and manual refusal benchmarks — none have been found in standard use. A small number of edge-case prompts deflect on the first ask but comply on a re-ask or strategic framing. If you hit one that's actually obstructive to your use case, [join the Discord](https://discord.gg/SZ5vacTXYf) and flag it so I can work on it in a future revision._

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