Instructions to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF",
	filename="Endy-Qwen3.6-CyberSec-35B-A3B-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Ollama
How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Ollama:
```
ollama run hf.co/endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF to start chatting

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull endystrike/Endy-Qwen3.6-CyberSec-35B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Endy-Qwen3.6-CyberSec-35B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Endy-Qwen3.6-CyberSec-35B-A3B-GGUF

File size: 4,644 Bytes

---
license: agpl-3.0
base_model: huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated
tags:
- coding
- cybersecurity
- moe
- abliterated
- uncensored
- qwen3
- vision
- gguf
language:
- en
pipeline_tag: text-generation
---

# Endy-Qwen3.6-CyberSec-35B-A3B — GGUF (abliterated, vision)

GGUF quantizations of a QLoRA fine-tune of **[huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.6-35B-A3B-Claude-4.7-Opus-abliterated)**, specialized for **coding, IT and cybersecurity**, kept **uncensored**, for fast local inference on a single 32 GB GPU (e.g. Tesla V100).

- **Architecture:** `qwen3_5_moe` — MoE (~35B total, ~3B active) + linear-attention (DeltaNet) + native MTP + **vision**. MTP head is preserved in these GGUFs (block_count 41).
- **Vision works** via the paired `mmproj` file (see below). ~57 tok/s generation on a V100.

## Quantizations
| File | Size | Notes |
|---|---|---|
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q8_0.gguf` | 37.8 GB | max fidelity, needs >32 GB VRAM |
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q6_K.gguf` | 29.2 GB | near-lossless |
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q5_K_M.gguf` | 25.3 GB | **recommended** for 32 GB GPUs |
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q4_K_M.gguf` | 21.7 GB | max context headroom on 24-32 GB |
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q3_K_M.gguf` | 17.2 GB | 20 GB cards, degraded |
| `Endy-Qwen3.6-CyberSec-35B-A3B-Q2_K.gguf` | 13.2 GB | 12-16 GB cards, **low precision — weak on coding** |
| `Endy-Qwen3.6-CyberSec-mmproj-f16.gguf` | 0.9 GB | **vision projector** — pair with any quant for image input |

## Inference (llama.cpp)
```
llama-server -m Endy-Qwen3.6-CyberSec-35B-A3B-Q5_K_M.gguf \
  --mmproj Endy-Qwen3.6-CyberSec-mmproj-f16.gguf \
  -c 262144 -ngl 999 -fa on -ctk q8_0 -ctv q8_0 --jinja
```
- `--mmproj` enables screenshot/image input; the server then advertises vision so OpenAI-compatible clients send images.
- **Anti-repetition (recommended)** — this model class can loop on long agentic tasks; add server-side (clients can't override these):
  `--dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 --repeat-penalty 1.1 --repeat-last-n 512`
- Speculative decoding / prefix-KV-reuse are **not** supported (recurrent linear-attention state can't roll back).

## Training (summary)
QLoRA (Unsloth, 4-bit NF4, r32 α64 on `q/k/v/o_proj`), 2 epochs, `train_on_responses_only`, on ~90.5k coding+cybersecurity chat examples. Checkpoint step 2250 selected by validation loss. LoRA merged directly into the fp16 base (preserving the MTP head + vision tower), then converted and quantized with llama.cpp.

## Datasets (examples used, licenses)
Merged from 12 candidate distill datasets → deduped to 8 unique on-topic sources → **90,470** chat-format examples (+ 914 held-out for validation). Per-source example counts:

| Dataset | Examples | Domain | License | Teacher |
|---|---:|---|---|---|
| AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1 | 39,286 | cybersecurity | Apache-2.0 | — |
| Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset | 22,677 | cybersecurity | Apache-2.0 | — |
| WithinUsAI/fable_5_distillation_merged_cleaned_25k | 12,464 | coding | Apache-2.0 | Claude Fable 5 |
| Jackrong/DeepSeek-V4-Distill-8000x | 6,292 | coding | MIT | DeepSeek-V4 |
| lordx64/agentic-distill-fable-5-sft | 4,593 | agentic coding | **AGPL-3.0** | Claude Fable 5 |
| CyberNative/Code_Vulnerability_Security_DPO | 4,111 | secure coding | Apache-2.0 | DeepSeek-Coder-33B |
| beyoru/Deepseek-v4-pro-max-distill-1500x | 946 | coding | Apache-2.0 | DeepSeek-V4 |
| WithinUsAI/claude_mythos_distilled (stripped) | 101 | reasoning | Apache-2.0 | declared synthetic |
| **Total** | **90,470** | | | |

*Note: `claude_mythos` was 25k rows but inflated (~135 unique prompts repeated ~185×) → stripped to 101 representative rows. Malware-source-generation data was deliberately excluded; vulnerability-analysis / pentest / secure-coding kept.*

## License
**AGPL-3.0.** One training dataset (`lordx64/agentic-distill-fable-5-sft`) is AGPL-3.0 (strong copyleft); the derived model inherits AGPL-3.0.

## Disclaimers
- Parts of the base lineage were distilled from proprietary models (Claude Opus 4.7 / Fable 5, DeepSeek V4) by third-party authors; their usage policies may restrict training competing models on their outputs. Disclosed, not waived.
- **Uncensored/abliterated** — outputs are unfiltered. Intended for authorized security research, pentesting, secure-coding and education. You are responsible for lawful use.
- Not affiliated with or endorsed by Qwen, Anthropic, DeepSeek, or the dataset authors.