Instructions to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use asmanovlev/veriloop-coder-e1-heretic-i1-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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

Use Docker

docker model run hf.co/asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "asmanovlev/veriloop-coder-e1-heretic-i1-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": "asmanovlev/veriloop-coder-e1-heretic-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

Ollama
How to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with Ollama:
```
ollama run hf.co/asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
```

Unsloth Studio

How to use asmanovlev/veriloop-coder-e1-heretic-i1-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 asmanovlev/veriloop-coder-e1-heretic-i1-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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for asmanovlev/veriloop-coder-e1-heretic-i1-GGUF to start chatting

How to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

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": "asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

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 "asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0" \
  --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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0
```

Lemonade

How to use asmanovlev/veriloop-coder-e1-heretic-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

Run and chat with the model

lemonade run user.veriloop-coder-e1-heretic-i1-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use asmanovlev/veriloop-coder-e1-heretic-i1-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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

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 asmanovlev/veriloop-coder-e1-heretic-i1-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

VeriLoop Coder E1 — Abliterated (i1, imatrix) GGUF

GGUF quants of VeriLoop Coder E1 (Qwen3.6-27B, coding-tuned) with the refusal direction abliterated (heretic / LoRA-merge), quantized with imatrix importance calibration.

⚠️ What "abliterated" means here

The model was run through heretic v1.4.0 (200 trials) with --export-strategy=ADAPTER, then the LoRA was merged into the base weights.
Partial abliteration: refusal rate dropped from ~95% to 82/100 on harmful_behaviors. The model is less censorious but still refuses many requests — Qwen 3.6's four PEFT-adapters distribute refusal patterns across multiple subspaces, so a single direction was hard to find.
KL divergence ≈ 0.0003 — general capability is preserved; only the refusal direction is nudged.
Use at your own discretion; the weights are provided as-is.

Files

File	Quant	Size	Notes
`VeriLoop-Coder-E1-Abliterated-Q8_0.gguf`	Q8_0	26.6 GB	Reference (no imatrix needed)
`abl_iq4_nl.gguf`	IQ4_NL	14.7 GB	Best quality/size balance
`abl_iq4_xs.gguf`	IQ4_XS	14.1 GB	Faster, slightly lower quality
`abl_iq3_xxs.gguf`	IQ3_XXS	10.4 GB	Good for 12-16 GB VRAM
`abl_iq2_xxs.gguf`	IQ2_XXS	7.9 GB	Fits 8 GB VRAM, quality drops
`imatrix.dat`	—	10 MB	Importance matrix used for IQ quants

All IQ quants were produced with the included imatrix.dat (code-focused calibration dataset).

Original model

Base: VeriLoop Coder E1 (Qwen3.6-27B)
SWE-bench Verified: 85.2% | SWE-bench Pro: 62.4% | Terminal-Bench 2.0: 76.4%

Usage (llama.cpp)

llama-cli -m abl_iq4_nl.gguf -p "def fib(n):" -n 64
# or with a server:
llama-server -m abl_iq4_nl.gguf -c 8192 --port 8080

imatrix.dat can be re-applied with llama-quantize --imatrix imatrix.dat if you want to re-quantize.

License

Apache-2.0 (same as the original).

Downloads last month: 8,639

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

8-bit

Model tree for asmanovlev/veriloop-coder-e1-heretic-i1-GGUF

Base model

Qwen/Qwen3.6-27B

Finetuned

tsinghua-sigs-robot-lab/veriloop-coder-e1

Quantized

(3)

this model