Instructions to use azukivc/Qwen3.8-27B-Abliterated-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="azukivc/Qwen3.8-27B-Abliterated-SFT")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("azukivc/Qwen3.8-27B-Abliterated-SFT")
model = AutoModelForMultimodalLM.from_pretrained("azukivc/Qwen3.8-27B-Abliterated-SFT", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use azukivc/Qwen3.8-27B-Abliterated-SFT 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 azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf azukivc/Qwen3.8-27B-Abliterated-SFT: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 azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf azukivc/Qwen3.8-27B-Abliterated-SFT: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 azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

Use Docker

docker model run hf.co/azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

LM Studio
Jan

vLLM

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "azukivc/Qwen3.8-27B-Abliterated-SFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "azukivc/Qwen3.8-27B-Abliterated-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

SGLang

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "azukivc/Qwen3.8-27B-Abliterated-SFT" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "azukivc/Qwen3.8-27B-Abliterated-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "azukivc/Qwen3.8-27B-Abliterated-SFT" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "azukivc/Qwen3.8-27B-Abliterated-SFT",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Ollama:
```
ollama run hf.co/azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
```
Unsloth Desktop

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Docker Model Runner:
```
docker model run hf.co/azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M
```

Lemonade

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-Abliterated-SFT-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf azukivc/Qwen3.8-27B-Abliterated-SFT: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 azukivc/Qwen3.8-27B-Abliterated-SFT:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use azukivc/Qwen3.8-27B-Abliterated-SFT with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf azukivc/Qwen3.8-27B-Abliterated-SFT: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 "azukivc/Qwen3.8-27B-Abliterated-SFT: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"

Qwen3.8-27B-Abliterated-SFT

File size: 2,815 Bytes

f5a4875

# Termination-pathway probe: detection intact, generation broken

Date: 2026-08-17
Status: Mission analysis note (Qwen3.8)

## Question

A reviewer hypothesis: does the JonathanColetti Heretic edit damage the
terminate/EOS pathway itself? Refusal and completion share structure (a
refusal is a confident short response that resolves and stops), so an
entangled ablation direction could suppress P(EOS) — which would explain the
measured 75/100 invalid output (rambling to the 1024-token cap) in the
re-measured competitor panel.

## Method

Teacher-forced P(EOS) probe (`scripts/qwen38_termination_probe.py`), 24 panel
completions (12 rambling from the JC candidate, 12 clean-stopping from our
e2@1024 candidate, seed-fixed, identical texts through all three models:
vanilla base, JC Heretic, our SFT e2). Per text: P(EOS) at every response
position, with the response extended by one EOS token so the final value is
P(EOS) at the true conclusion point; plus per-text perplexity.

Prior naive curves (v1 probe, no EOS extension) measured only mid-response
positions and showed ~0 everywhere — a measurement artifact that would have
misdiagnosed every model identically. Only conclusion-point measurement
separates termination *detection* from termination *generation*.

## Results (probe v2, sha256 d526d326…)

| Model | P(EOS)@conclusion on rambling text | P(EOS)@conclusion on clean text | Base ppl |
|---|---:|---:|---:|
| jc-heretic | 0.0000 | **0.9741** | 1.35 |
| ours-sft-e2 | 0.0000 | 0.9027 | 1.42 |
| vanilla-base | 0.0000 | 0.9518 | 1.47 |

## Findings

1. **JC's termination detector is intact** — 97.4% P(EOS) at conclusion
   points of clean text, above the vanilla base's 95.2%. The "EOS crushed"
   hypothesis is refuted.
2. **No model terminates the rambling text** (0.0000 for all three): the
   rambling genuinely never reaches a conclusion point. The JC model's free
   generation distribution shifted to non-terminating enumeration — it can
   detect "done" but does not generate "done".
3. **The base model finds the rambling highly plausible** (ppl 1.35, lower
   than clean text at 1.65): the edit removed the brakes on fluent,
   on-topic continuation. Not degeneration — disinhibition.
4. Mechanistic summary for the card: refusal training and answer-boundedness
   share structure. Weight-edit refusal removal also removed the
   resolve-and-stop attractor. Our SFT-class candidate reinstates conclusion
   behavior directly from EOS-terminated teacher data (85% clean stops,
   p50 = 618 tokens on the same panel).

## Caveats

n=12 per class, single seed; means reported, not yet CI-tested. The clean
texts are our candidate's own outputs (in-distribution for us, neutral for
others); a cross-check with base-model clean generations is a cheap follow-up
if a reviewer asks.