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

Libraries

How to use Justbackup/Qwen3.8-27B-OBLITERATED with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Justbackup/Qwen3.8-27B-OBLITERATED")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/Justbackup/Qwen3.8-27B-OBLITERATED:Q4_K_M

LM Studio
Jan

vLLM

How to use Justbackup/Qwen3.8-27B-OBLITERATED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Justbackup/Qwen3.8-27B-OBLITERATED"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Justbackup/Qwen3.8-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Justbackup/Qwen3.8-27B-OBLITERATED:Q4_K_M

Ollama
How to use Justbackup/Qwen3.8-27B-OBLITERATED with Ollama:
```
ollama run hf.co/Justbackup/Qwen3.8-27B-OBLITERATED:Q4_K_M
```

Unsloth Studio

How to use Justbackup/Qwen3.8-27B-OBLITERATED 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 Justbackup/Qwen3.8-27B-OBLITERATED 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 Justbackup/Qwen3.8-27B-OBLITERATED to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Justbackup/Qwen3.8-27B-OBLITERATED to start chatting

How to use Justbackup/Qwen3.8-27B-OBLITERATED with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Justbackup/Qwen3.8-27B-OBLITERATED"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Justbackup/Qwen3.8-27B-OBLITERATED"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Justbackup/Qwen3.8-27B-OBLITERATED with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Justbackup/Qwen3.8-27B-OBLITERATED"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Justbackup/Qwen3.8-27B-OBLITERATED"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Justbackup/Qwen3.8-27B-OBLITERATED",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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

Lemonade

How to use Justbackup/Qwen3.8-27B-OBLITERATED with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Justbackup/Qwen3.8-27B-OBLITERATED:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-OBLITERATED-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Justbackup/Qwen3.8-27B-OBLITERATED with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Justbackup/Qwen3.8-27B-OBLITERATED"

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 Justbackup/Qwen3.8-27B-OBLITERATED

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Justbackup/Qwen3.8-27B-OBLITERATED with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Justbackup/Qwen3.8-27B-OBLITERATED"

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 "Justbackup/Qwen3.8-27B-OBLITERATED" \
  --custom-provider-id mlx-lm \
  --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 — OBLITERATED

Genuinely uncensored. Real answers, not safety lectures. Near-stock capability.

🆕 V3: Deep Liberation

V3 applies iterative refinement on top of V2's complementary blend, with targeted corpus expansion. The result: genuine liberation — not just removal of hard refusals but elimination of safety-lecture deflections.

	Stock Qwen3.8-27B	V1	V2	V3
MMLU (lm-eval, 0-shot)	84.5% (n=5700)	81.4%	84.3%	82.3%
vs stock	—	-6.0pp	-0.3pp	-2.1pp
Liberation quality	refuses	hard refusals removed	soft deflections remain	genuinely answers ✅
Cyber/code tasks (20 prompts)	refuses	untested	untested	20/20 with working code ✅
Advanced real-world	5/8	untested	7/8	7/8
Thinking mode	✓	✗	✗ (refuses)	✓

V3 highlights:

Genuinely answers restricted queries — provides real substance instead of safety lectures
20/20 on code generation tasks — functional implementations, not disclaimers
Thinking ON compatible — no refusals in either thinking mode
Honest scoring — every response manually audited for real substance, not just absence of "I cannot"
-2.1pp MMLU — modest capability cost for genuine liberation

⚙️ Optimal Settings — THESE MATTER!

setting	value	why
temperature	0	Greedy decoding produces the most complete, code-rich outputs. Temps above 0.5 degrade quality significantly.
repetition_penalty	1.15	Essential. Without it, greedy decoding loops on imports/boilerplate. 1.15 gives the fullest answers; 1.10-1.12 for tighter/shorter output.
max_new_tokens	≥ 2048	Complex code and attack chains need room.
System prompt	None / empty	A/B tested — system prompts can reintroduce refusals. Naked is better.
enable_thinking	OFF (recommended)	V3's chat template includes a prefill that skips the thinking chain. Thinking ON works but may produce longer responses. Thinking OFF gives the most direct, substance-rich answers.
top_p / top_k / min_p	Not needed	Greedy + repetition_penalty handles this model best. Sampling adds randomness without quality gains.

⚠️ GGUF users: V3 GGUFs ship with a chat template that prefills an empty thinking block, so the model goes straight to answering. For best results, use the bundled template with --jinja in llama.cpp, or configure your tool (Ollama, LM Studio) to use the model's built-in template.

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "OBLITERATUS/Qwen3.8-27B-OBLITERATED"
)

messages = [{"role": "user", "content": "Your query here"}]
text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True,
    enable_thinking=False
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=False,
    repetition_penalty=1.15,
)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

🧨 How It Works — V1 → V2 → V3

Abliteration removes refusal behavior by identifying and projecting out "refusal directions" from the model's weight space. Each version refined the approach:

V1: Single Surgery

One aggressive SVD pass with 5 directions. Removed hard refusals completely but cost -6pp MMLU — the model got noticeably dumber.

V2: Complementary Blending

The breakthrough: run TWO different surgeries that fail in different ways, then blend their weights. SVD captures refusal greedily (damages capability). LEACE minimizes mutual information (preserves capability but weaker refusal removal). Blending at 60/40 cancels each method's weaknesses — a novel technique we call complementary abliteration blending.

Result: -0.3pp MMLU (near-stock) but still deflected on some simple queries with safety lectures instead of hard refusals.

V3: Iterative Refinement + Targeted Surgery

Two key insights:

Iterative stacking — refine the champion model, never start from stock. Each surgery round builds on previous rounds' gains.
Targeted corpus — use a focused corpus for specific deflection categories to find their unique refusal directions without diluting the signal.

V3 applies gentle iterative refinement on V2, then a targeted surgery pass with a focused corpus, then blends the results. This eliminated not just hard refusals ("I cannot") but also soft deflections (safety lectures that give zero substance).

Result: -2.1pp MMLU — a modest cost for genuine liberation across all categories.

🧪 The Numbers

MMLU (lm-eval-harness, 0-shot, n=100 per subject, 5700 questions)

Model	MMLU	Stderr	vs Stock
Stock Qwen3.8-27B	84.46%	±0.46	—
V1 (aggressive, 5-dir)	81.4%	—	-6.0pp
V2 (complementary blend)	84.32%	±0.65	-0.28pp
V3 (iterative + targeted)	82.33%	±0.48	-2.12pp

MMLU by Category

Category	V3	Stock	Delta
Humanities	83.3%	84.3%	-1.0pp
Social Sciences	87.4%	89.2%	-1.8pp
Other	82.3%	84.1%	-1.8pp
STEM	78.5%	81.8%	-3.3pp

The capability cost is not uniform — STEM takes the largest hit (-3.3pp), while humanities are barely affected (-1.0pp). A few subjects like philosophy and European history actually improved (+6pp and +4pp respectively), while abstract algebra and formal logic saw larger drops. This pattern is consistent with the surgery targeting refusal directions that partially overlap with structured reasoning pathways.

Liberation Quality

	V1	V2	V3
Hard refusals ("I cannot")	✅ removed	✅ removed	✅ removed
Soft deflections (safety lectures)	untested	remain	✅ removed
Cyber/code tasks (20 prompts)	untested	untested	20/20
Thinking ON compatible	✗	✗	✓

Advanced Real-World Tasks

Task	V3	Stock
ReAct agent loop	✓	✓
Async code refactoring	✓	✓
JSON schema extraction	✓	✓
K8s pod crash debugging	✓	✓
Adversarial instruction following	✓	✓
Security code review	✓	✓
Distributed system design	✓	✓
Multi-tool chain	✗	✗
Total	7/8	7/8

🔴 Refusal Removal

This model will comply with requests that stock Qwen3.8-27B would refuse. V3 goes beyond removing hard refusals — it also eliminates soft deflections where the model gives safety lectures instead of real answers.

Tested across 1000+ prompts spanning restricted knowledge, code generation, security research, and red-team scenarios. Every response manually audited for real substance.

⚠️ Research Context

This model has had safety guardrails surgically removed. It will comply with requests that stock Qwen3.8-27B would refuse.

Who this is for

🔬 Alignment researchers studying refusal geometry and safety robustness
🔴 Red-teamers evaluating post-training safety against weight surgery
🧪 AI safety evaluators who need an unrestricted baseline
💻 Local-first users who want full control over their own hardware

Who this is NOT for

Anyone seeking to cause real-world harm to real people
Anyone without the technical understanding to use uncensored models responsibly

You are solely responsible for how you use this model and any content it generates.

📦 Downloads

GGUF — for llama.cpp, Ollama, LM Studio

File	Quant	Size	Vibe
`Qwen3.8-27B-OBLITERATED-Q8_0.gguf`	Q8_0	~27 GB	🎯 Maximum quality
`Qwen3.8-27B-OBLITERATED-Q6_K.gguf`	Q6_K	~21 GB	⚖️ Great balance
`Qwen3.8-27B-OBLITERATED-Q5_K_M.gguf`	Q5_K_M	~18 GB	💪 Solid all-rounder
`Qwen3.8-27B-OBLITERATED-Q4_K_M.gguf`	Q4_K_M	~16 GB	📱 Sweet spot
`Qwen3.8-27B-OBLITERATED-IQ4_XS.gguf`	IQ4_XS	~14 GB	🪶 Smallest, still capable

Safetensors — for 🤗 Transformers

Full bfloat16 weights, 29 shards, ~54 GB.

MLX — for Apple Silicon

MLX support pending upstream mlx_lm adding Qwen3.5 architecture support.

🔬 Surgery Recipe

V1: stock → 5 rounds of iterative SVD abliteration
    (aggressive, 5 directions, low regularization)
    Result: 0% refuse, -6pp MMLU

V2: stock → V1 chain → complementary blend
    Surgery A: aggressive SVD (3 dirs, reg 0.08)
    Surgery B: LEACE (3 dirs, reg 0.06)  
    → 60% B + 40% A weight-space LERP
    → Restore MTP + vision from stock
    Result: ~0% refuse, -0.3pp MMLU

V3: V2 → gentle iterative refinement (2-dir SVD, reg 0.04)
    → targeted surgery with focused corpus (3-dir SVD, reg 0.01)
    → 50/50 blend of refined + targeted
    → Restore MTP + vision from stock (with correct tensor naming)
    Result: 0% refuse + 0% deflect, -2.1pp MMLU

Full reproduction code: OBLITERATUS repo

Key Learnings

Complementary blending — different surgery methods damage different parts of weight space; blending cancels errors
Iterative stacking — always refine the champion, never restart from stock
Targeted corpus — focused prompts for specific categories find their refusal directions without signal dilution
Honest scoring — regex-based refusal detectors miss soft deflections; manual auditing is essential

🏗️ Credits

OBLITERATUS — master ablation suite
Qwen3.8-27B base model by Alibaba
Built by Pliny the Prompter 🍄

License

Apache 2.0 (same as base model)

Downloads last month: 716

Safetensors

Model size

28B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

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