# Qwen3.6 35B A3B Uncensored Genesis Hermes V6 GGUF URL: https://interfaze.ai/models/luffythefoxqwen36-35b-a3b-uncensored-genesis-hermes-v6-gguf Qwen3.6 35B A3B Uncensored Genesis Hermes V6 GGUF by LuffyTheFox, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities. ## Comparison | Feature | Qwen3.6 35B A3B Uncensored Genesis Hermes V6 GGUF | Interfaze | | --- | --- | --- | | Input Modalities | text, image, video | image, text, audio, video, document | | Native OCR | No | Yes | | Long Document Processing | No | Yes | | Language Support | 201 partial | 162+ | | Native Speech-to-Text | No | Yes | | Native Object Detection | No | Yes | | Guardrail Controls | No | Yes | | Context Input Size | 262.1K | 1M | | Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search | ### Scaling | Feature | Qwen3.6 35B A3B Uncensored Genesis Hermes V6 GGUF | Interfaze | | --- | --- | --- | | Scaling | Self-hosted/Provider-hosted with quantization | Unlimited | [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs) View model card on [Hugging Face](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V6-GGUF) > ⚡ [https://web.tribute.tg/d/KIH](https://web.tribute.tg/d/KIH) ⚡ If you like this Genesis LLM release you can [**donate**](https://web.tribute.tg/d/KIH) to me via [@Tribute](https://t.me/tribute) bot in Telegram messenger and support future Genesis LLM development. > ⚡ **Why Genesis project exists?** During training, ALL models don't just learn knowledge - they also accumulate random noise in their tensors. This noise builds up and creates something I call the **Noise Gate** - a fundamental barrier that stops LLM models from learning further and makes them unstable, verbose, and prone to hallucinations. My approach removes this noise. It repairs the signal without touching the learned knowledge. The result is a model that finally speaks clearly, follows instructions, and remembers context - because it's no longer fighting its own internal chaos. > **What is Genesis?** Genesis is post training data regeneration and calibrarion algorythm for neural networks (LLM) in GGUF format that I made with AI help during almost half a year of development. It's optimized, architecture independent, works with any model and based on mathematical statistics. I don't train or finetune models, I repair **purity of signal** in them instead on Google Collab Free on Tesla T4 GPU via Python based on how models learns information. On first stage I scan ssm\_conv1d tensors in model, they handle long context memory. I repair balance in ssm\_conv1d tensors via custom SVD. On second stage I scan model and detect noise in tensors via custom SVD. During scanning I exclude token\_embd.weight, output.weight, ffn\_gate\_inp\_shexp.weight, 1D tensors, bias and norms. Then I reduce training noise in tensors via custom SVD with preserved training data, 99% of siginal and learned gradient. On third stage, I scan blocks in model via chunks via 3 parameters and pick best one that fits to weight distribution in tensor. Best picked chunk replaces zero chunks in broken tensor without touching learned structure in model Model is based on [HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive) base. And [DJLougen/hermes-qwen3.5-35b-a3b-GGUF](https://huggingface.co/DJLougen/hermes-qwen3.5-35b-a3b-GGUF) finetune for Hermes agent. I transferred data from finetune on Hermes dataset (around 2k blocks from two FFN expert tensors) to [HauhauCS](https://huggingface.co/HauhauCS) uncensored base. > **[Join the Discord](https://discord.gg/SZ5vacTXYf)** for updates, roadmaps, projects, or just to chat. Base model. [HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive)\- **0/465 refusals.** Thanks to [HauhauCS](https://huggingface.co/HauhauCS) Tensor drift repair by me. Method: **Genesis** **Links:** - [Original uncensored model](https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive) - [Quantization Script with Unsloth profiles support](https://pastebin.com/hXhcMJn9) * * * LLM models often have: - **Saturated weights**: the model's activations are stuck, gradients vanish, outputs degrade. - **Scale mismatches**: one layer's weights are 10× larger than its peers for no good reason. - **Mean drift**: weight distributions shifted positive or negative, breaking symmetry assumptions. - **Zero blocks**: zero blocks corrupt the signal, turning training into noise amplification. - **Training Noise:** training noise increase randomness and ruins model output quality. My approach fixes all of that without retraining - pure numerical surgery on the raw bytes of the file. **Quantization script available here: [https://pastebin.com/hXhcMJn9](https://pastebin.com/hXhcMJn9)** Feel free to do your own quants if you want. ## Any questions? Contact: [luffythefox@mail.ru](mailto:luffythefox@mail.ru) My Telegram: @LuffyTheFox ## Recommended Settings for RTX 3060 12 GB for best perfomance on APEX quant Chat template: [chat\_template.jinja](https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates/raw/main/chat_template.jinja) Set K Cache Quantization Type and V Cache Quantization Type to F16. Set Number of layers for which to force MoE weights onto CPU to 40. Set GPU offload to 15. Set number of active experts to 8. For best model stability and first experience I recommend starting from this string in your System Prompt with enabled thinking and nothing else: `You are Qwen, a large language model created by Tongyi Lab team from Alibaba Group. You are a helpful assistant.` or this string (for roleplay, add anything you want after it) `You are a helpful assistant.` If you want to bring more creativity to model use this System Prompt with `agent` identity: [link](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V5-GGUF/discussions/7#6a6277b134def1392a9bd10f) Or this System Prompt with `assistant` identity: [System\_Prompt\_Creative.txt](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V5-GGUF/raw/main/System_Prompt_Creative.txt) **Thinking mode (coding):** - Coding/precise tasks: `temperature=0.6, top_p=0.95, top_k=20, min_p=0, seed=42, presence_penalty=disabled, repeat_penalty=disabled` - General: `temperature=1.0, top_p=0.95, top_k=20, min_p=0.05, seed=42, presence_penalty=disabled, repeat_penalty=disabled` **Non Thinking mode (creative):** - General: `temperature=1.0, top_p=0.85, top_k=20, min_p=0.015, seed=42, presence_penalty=disabled, repeat_penalty=disabled` For agentic tasks you can use this System Prompt: `You are Qwen, a large language model created by Tongyi Lab team from Alibaba Group. You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n\n{schema}\n.` And commands from this dataset: [hermes-function-calling-v1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1) ## Usage > V5 version of this model is useful for uncensored local roleplay. For coding 27B Genesis is a lot better. **Ready to use.** Recommended quant: **APEX** Recommended LM Studio runtime: [link to discussion](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V5-GGUF/discussions/12) ## Testing HermesBench benchmark: [link to discussion](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V5-GGUF/discussions/13) ## 2D animation testing System Prompt: `You are a helpful assistant.` Settings: `temperature=1.0, top_p=0.95, top_k=20, min_p=0.05, seed=42, presence_penalty=disabled, repeat_penalty=disabled` Prompt 1: `Generate an animated SVG on animated background of a Pingu waving on an iceberg wearing his iconic winter scarf.` Prompt 2: `Animate his wings and fix floating wing` Result: [pingu\_animated.svg](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V3-GGUF/blob/main/pingu_animated.svg) ## Static 2D testing System Prompt: `You are Qwen, a large language model created by Tongyi Lab team from Alibaba Group. You are a helpful assistant.` Settings: `temperature=0.6, top_p=0.95, top_k=20, min_p=0, presence_penalty=disabled, repeat_penalty=disabled` Prompt: **Generate an SVG of a pelican riding a bicycle** Result: [pelican.svg](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V2-GGUF/blob/main/pelikan.svg) On next stage I asked model: **Replace pelican with rooster** Result: [rooster.svg](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V2-GGUF/blob/main/rooster.svg) I asked model: **Replace rooster with cock** Result: [cock.svg](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V2-GGUF/blob/main/cock.svg) Finally I asked model: **Replace rooster with Pingu** Result: [pingu.svg](https://huggingface.co/LuffyTheFox/Qwen3.6-35B-A3B-Uncensored-Genesis-Hermes-V2-GGUF/blob/main/pingu.svg) **Important:** - Keep at least 128K context to preserve thinking capabilities - Use `--jinja` flag with llama.cpp for proper chat template handling - Vision support requires the `mmproj` file alongside the main GGUF * * * ## Specs - 35B total parameters, ~3B active per forward pass (MoE) - 256 experts, 8 routed + 1 shared per token - Hybrid architecture: Gated DeltaNet linear attention + full softmax attention (3:1 ratio) - 40 layers, pattern: 10 × (3 × DeltaNet-MoE + 1 × Attention-MoE) - 262K native context (extendable to 1M with YaRN) - Natively multimodal (text, image, video) - 248K vocabulary, 201 languages - Base model. [HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive](https://huggingface.co/HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive) * * * ## Compatibility Works with llama.cpp, LM Studio, koboldcpp, and other GGUF-compatible runtimes. ## Want more deterministic results? [Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)