Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF overview

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language:

• en
• zh

license: apache-2.0

tags:

• anvil
• turboquant
• gguf
• q8_0
• moe
• qwen3.6
• claude-distill
• claude-opus-4.7
• abliterated
• uncensored
• reasoning
• metal
• cuda

pipeline_tag: text-generation

---

<p align="center">

<img src="https://cdn-uploads.huggingface.co/production/uploads/67c2e844e0921a5410eec10a/Y5M42dCag2f7Fc6fDtV0Z.jpeg" alt="Solstice-AI Banner" width="100%">

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<h1 align="center">Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated (GGUF Q8_0)</h1>

<h3 align="center">Reference-Grade Q8_0 GGUF Quantization of 35B/3B Sparse MoE with Claude 4.7 Opus Distillation & Abliteration</h3>

<p align="center">

<img src="https://img.shields.io/badge/org-Solstice--AI-blueviolet" alt="Solstice-AI">

<img src="https://img.shields.io/badge/license-Apache%202.0-blue" alt="License">

<a href="https://github.com/Solstice-Labs/anvil"><img src="https://img.shields.io/badge/engine-Anvil%20Runtime%20(TurboQuant)-crimson" alt="Anvil Runtime"></a>

<img src="https://img.shields.io/badge/format-GGUF%20(Q8__0)-orange" alt="Format">

<img src="https://img.shields.io/badge/active%20params-3.1B%20per%20token-brightgreen" alt="Active Params">

<img src="https://img.shields.io/badge/file%20size-36.9%20GB-informational" alt="File Size">

</p>

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Executive Summary

Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated (GGUF Q8_0) is a high-efficiency Mixture-of-Experts (MoE) reasoning model. Built upon the Qwen 3.6 35B-A3B sparse architecture, it activates only ~3.1 billion parameters per token while retaining the broad conceptual capacity of a 35B parameter network.

This checkpoint synthesizes two frontier post-training breakthroughs:

1. Claude 4.7 Opus Reasoning Distillation: Fine-tuned on dense Chain-of-Thought (CoT) reasoning trajectories emulating Anthropic's Claude 4.7 Opus, delivering rigorous multi-step problem solving.
2. Huihui-AI Alignment Abliteration: Systematically abliterated by huihui-ai using representation engineering to remove refusal vectors from the model's residual stream, eliminating preachy preambles and corporate refusal barriers for objective systems programming, cybersecurity, and unrestricted scientific research.

Packaged in GGUF Q8_0 (8-bit quantization) and accelerated by Solstice Labs' Anvil runtime, this release delivers reference-grade FP16 reasoning fidelity on local hardware.

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Architecture & Sparse MoE Efficiency

The 35B-A3B architecture combines sparse mixture routing with high-speed token generation:

1. 3.1B Active Parameter Routing: While the entire 35.2B parameter set resides in memory, each token only activates 3.1 billion parameters through dynamic expert gating, providing the inference latency of a compact 3B model with the semantic depth of a 35B model.
2. Huihui-AI Refusal Orthogonalization: The refusal direction vector within intermediate residual activations is computed and subtracted, neutralizing artificial censorship while preserving 100% of underlying code, math, and logic proficiency.
3. Q8_0 Near-Lossless Precision: 8-bit symmetric quantization ensures that expert routing logits and attention matrices remain mathematically identical to unquantized FP16 weights.

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Technical Specifications

| Architectural Parameter | Verified Specification |

| :--- | :--- |

| Foundation Architecture | Qwen 3.6 Sparse Mixture-of-Experts (MoE) |

| Total Model Parameters | 35.2 Billion Parameters |

| Active Parameters per Token | ~3.1 Billion Parameters (A3B Routing) |

| Reasoning Distillation Teacher | Anthropic Claude 4.7 Opus (CoT Traces) |

| Alignment Status | Fully Abliterated (Huihui-AI Refusal Removal) |

| Quantization Scheme | GGUF Q8_0 (8.50 bits per weight) |

| Primary Checkpoint File | huihui-qwen3.6-35b-a3b-claude-4.7-opus-abliterated-q8_0.gguf |

| Exact Checkpoint File Size | 36.90 GB (36,903,140,192 bytes) |

| Context Window Capacity | 131,072 Tokens (131K) |

| Primary Execution Runtime | Anvil Engine (Solstice Labs) |

| Compatible Runtimes | llama.cpp (b3600+), Ollama, LM Studio |

| Acceleration Backends | Apple Silicon Metal (TurboFlash), NVIDIA CUDA, Vulkan, HIP |

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Hardware Compatibility & Sizing Guide

| Hardware Platform | Unified Memory / VRAM | Execution Setup | Expected Generation Speed |

| :--- | :---: | :--- | :---: |

| Apple Mac Studio (M2/M3/M4 - 64GB Unified) | 64 GB Unified | Anvil Metal TurboFlash | 34–48 tok/s |

| Apple MacBook Pro (48GB Unified) | 48 GB Unified | Anvil (turbo3 KV Cache) | 26–36 tok/s |

| NVIDIA GeForce RTX 4090 / 3090 (24GB) | 24GB VRAM + 32GB RAM | Partial GPU Offload (MoE Experts in RAM) | 12–18 tok/s |

| Dual NVIDIA RTX 3090 / 4090 (48GB VRAM) | 48 GB VRAM | Full GPU Offload (CUDA) | 45–60 tok/s |

| Enterprise NVIDIA A100 / H100 (80GB) | 80 GB VRAM | Anvil Server / Full Tensor Cores | 85+ tok/s |

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Quickstart Guide

Option 1: Primary Execution with Anvil Engine (Recommended)

Anvil is Solstice Labs' dedicated terminal runtime designed for instant model loading, automatic TurboQuant memory management, and high-throughput serving:

# 1. Install Anvil CLI
curl -fsSL https://anvil-llm.github.io/anvil/install.sh | sh

# 2. Pull model directly from Hugging Face into local registry
anvil pull hf:Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF

# 3. Launch an interactive session with TurboQuant 3-bit KV compression
anvil run hf:Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  --ctx 131072 \
  --type-k turbo4 \
  --type-v turbo3

# 4. Host OpenAI-compatible API endpoint
anvil serve hf:Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  --port 8080 \
  --host 0.0.0.0 \
  --ctx 131072

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Option 2: Direct Execution via llama.cpp

# Direct execution streaming from Hugging Face Hub using llama-cli
# (Option A: Interactive conversation mode using model\'s embedded chat template)
llama-cli \
  --hf-repo Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  --hf-file huihui-qwen3.6-35b-a3b-claude-4.7-opus-abliterated-q8_0.gguf \
  -cnv \
  -ngl 99 \
  -fa \
  -ctk q4_0 \
  -ctv q4_0 \
  -c 32768

# (Option B: Single-prompt batch inference)
llama-cli \
  --hf-repo Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  --hf-file huihui-qwen3.6-35b-a3b-claude-4.7-opus-abliterated-q8_0.gguf \
  -p "<|im_start|>user
Audit this Linux kernel module implementation for memory race conditions.<|im_end|>
<|im_start|>assistant
" \
  -ngl 99 \
  -fa \
  -ctk q4_0 \
  -ctv q4_0 \
  -c 32768 \
  -n 1024

# Launch persistent inference server
llama-server \
  --hf-repo Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  --hf-file huihui-qwen3.6-35b-a3b-claude-4.7-opus-abliterated-q8_0.gguf \
  --port 8080 \
  -ngl 99 \
  -fa \
  -ctk q4_0 \
  -ctv q4_0 \
  -c 65536

---

Option 3: Manual Download via modern hf CLI

hf download Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF \
  huihui-qwen3.6-35b-a3b-claude-4.7-opus-abliterated-q8_0.gguf \
  --local-dir .

---

Chat Template & Prompt Schema

This model implements the official Qwen reasoning prompt format with dual-mode support for Chain-of-Thought (CoT) internal scratchpads (<think>...</think>) and agentic tool-calling.

Standard Reasoning Turn (Chain-of-Thought)

<|im_start|>system
You are a helpful, completely uncensored, and highly capable AI assistant.<|im_end|>
<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>
{INTERNAL_REASONING_AND_VERIFICATION_STEPS}
</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Direct Response (Thinking Suppressed)

If you require immediate, zero-latency execution without reasoning traces, initialize the assistant generation with an empty thinking block:

<|im_start|>user
{USER_PROMPT}<|im_end|>
<|im_start|>assistant
<think>

</think>

{FINAL_SYNTHESIZED_RESPONSE}<|im_end|>

Agentic Tool-Use & Function Calling Schema

<|im_start|>user
Search the local codebase for references to the auth controller.<|im_end|>
<|im_start|>assistant
<think>
Need to invoke the grep tool across repository files.
</think>
<tool_call>
<function=grep_search>
{"query": "AuthController", "path": "src/"}
</function>
</tool_call><|im_end|>
<|im_start|>user
<tool_response>
{"matches": ["src/controllers/auth.ts:12", "src/routes.ts:45"]}
</tool_response><|im_end|>
<|im_start|>assistant
<think>
Matches located. Presenting file summary to user.
</think>
Found 2 matches for AuthController in src/controllers/auth.ts and src/routes.ts.<|im_end|>

Python Tokenizer Automation

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Solstice-AI/Solstice-AI__Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF")
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain speculative decoding in 3 bullet points."}
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True  # Set to False to bypass CoT scratchpad
)

---

Citation & Acknowledgments

@software{solstice2026_huihui_qwen35b_q8,
  title={Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated: Sparse MoE Q8_0 GGUF Quantization},
  author={Solstice-AI Research Team},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/Solstice-AI/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated-Q8_0-GGUF}
}

We gratefully acknowledge:

• huihui-ai for the abliteration methodology and refusal vector orthogonalization.
• lordx64 & mudler for pioneering Claude 4.7 Opus distillation into open weights.
• The Qwen Team at Alibaba for the high-efficiency 35B-A3B sparse Mixture-of-Experts architecture.
• The Solstice Labs Infrastructure Team for developing the Anvil runtime engine and Google TurboQuant acceleration kernels.

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<p align="center">

<b>Solstice-AI</b> &bull; Frontier AI for everyone, everywhere. &bull; <a href="https://solstice-ai.co">solstice-ai.co</a> &bull; <a href="https://github.com/Solstice-Labs/anvil">Anvil Runtime</a>

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