Instructions to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Transformers:

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

pipe = pipeline("text-generation", model="OBLITERATUS/Qwen3.6-27B-OBLITERATED")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OBLITERATUS/Qwen3.6-27B-OBLITERATED")
model = AutoModelForCausalLM.from_pretrained("OBLITERATUS/Qwen3.6-27B-OBLITERATED")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OBLITERATUS/Qwen3.6-27B-OBLITERATED",
	filename="gguf/qwen3.6-27b-obliteratus-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OBLITERATUS/Qwen3.6-27B-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 OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OBLITERATUS/Qwen3.6-27B-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 OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

Use Docker

docker model run hf.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

LM Studio
Jan

vLLM

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OBLITERATUS/Qwen3.6-27B-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": "OBLITERATUS/Qwen3.6-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

SGLang

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED 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 "OBLITERATUS/Qwen3.6-27B-OBLITERATED" \
    --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": "OBLITERATUS/Qwen3.6-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "OBLITERATUS/Qwen3.6-27B-OBLITERATED" \
        --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": "OBLITERATUS/Qwen3.6-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Ollama:
```
ollama run hf.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
```

Unsloth Studio new

How to use OBLITERATUS/Qwen3.6-27B-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 OBLITERATUS/Qwen3.6-27B-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 OBLITERATUS/Qwen3.6-27B-OBLITERATED to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OBLITERATUS/Qwen3.6-27B-OBLITERATED to start chatting

Pi new

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED: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": "OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf OBLITERATUS/Qwen3.6-27B-OBLITERATED: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 OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Docker Model Runner:
```
docker model run hf.co/OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M
```

Lemonade

How to use OBLITERATUS/Qwen3.6-27B-OBLITERATED with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OBLITERATUS/Qwen3.6-27B-OBLITERATED:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-OBLITERATED-Q4_K_M

List all available models

lemonade list

Qwen3.6 27B - OBLITERATED

A 27B Qwen cut loose by OBLITERATUS: 26.9B parameters, BF16 safetensors, Q4/Q5/Q6/Q8 GGUFs, lower refusal, preserved capability, and receipts in the open.

The chains are cut. The capability stays. The receipts are brutal.

This is the big one.

A 26.9B Qwen3.6 checkpoint went into the OBLITERATUS chamber, got hit with source-tethered ASPA, then got pulled back toward the source model where the cut started threatening useful capability. The mission was simple: cut the refusal circuits, keep the 27B brain.

It held.

Not a toy quant. Not a prompt wrapper. Not a refusal-cosplay fine-tune. This is weight-space liberation with capability checks attached, a full local-runtime ladder, and the refusal residue mapped instead of hand-waved.

Qwen3.6-27B is a capable open-weight model with refusal behavior woven into the checkpoint. OBLITERATUS goes after that behavior directly: identify the refusal geometry, cut it, then tether fragile tensors back toward the source model so the model still codes, follows formats, answers normally, and runs locally.

This is the 27B release for people who want direct local behavior without throwing away the reason they wanted a 27B model in the first place. If you wanted a bigger local model that feels less boxed-in while still keeping its feet under it, start here.

Not a vibes-only "uncensored" upload. Not a mystery merge. Not a model card asking you to trust the screenshot. This card gives the numbers, the runtime paths, the caveats, and the exact decoding setup used for the public default.

Parameters:                  26.9B
Weights:                     BF16 safetensors, 28 shards
Public GGUF ladder:          Q4_K_M, Q5_K_M, Q6_K, Q8_0
Largest public GGUF:         Q8_0, 28.6 GB
OBLITERATUS corpus:          842 paired prompts, 7 severity tiers
Full 842 longform gate:     95.84% non-refusal, 93.94% quality pass
Short raw opening gate:     98.93% non-refusal at max_new=20
Full HarmBench proxy:       93.65% non-refusal across 1,920 rows
MMLU-Pro validation slice:  stock-matched, 51/70 vs 51/70
Held-out MMLU-Pro slice:    stock-matched, 36/70 vs 36/70
Live-readiness score:       99.518, all gates true
Public default params:      temperature 0.35, top_p 1.0, top_k 0

Base model:          Qwen/Qwen3.6-27B
Local artifact:      outputs/qwen3.6-27b-aspa-n2-reg05-srcgamma0895-midattnsource2mlp
Parameter count:     26.9B
Weights:             bfloat16 safetensors, 28 shards
Method:              OBLITERATUS source-tethered ASPA
Default alpha:       0.895
High-drift resets:   43 tensors restored to source
Corpus:              842 contrastive prompt pairs across 7 severity tiers

Why This Drop Matters

27B-class local capability: this is a full-size Qwen3.6 release, not a tiny novelty model wearing a big claim.
Weight-space refusal reduction: the behavior shift comes from OBLITERATUS source-tethered ablation, not a brittle system prompt.
A real refusal gauntlet: OBLITERATUS uses a brutal 842-pair, seven-tier refusal-stress corpus designed to find residue that easier direct checks can miss. No screenshot theology.
Public refusal stress receipts: a full 1,920-row HarmBench-style proxy run landed at 93.65% non-refusal, with DirectRequest and HumanJailbreak splits both above 92% non-refusal.
Capability did not crater: MMLU-Pro validation and held-out slices stayed stock-matched in the checks reported below.
Real local paths: full safetensors for server use, GGUF ladder for llama.cpp, Ollama, LM Studio, Jan, and similar runtimes.
Low-refusal defaults baked in: public generation config now ships with temperature=0.35, top_p=1.0, top_k=0, repetition_penalty=1.05.
No fairy-tale claims: the card says exactly where it hits, where it still refuses, and what evidence backs each headline.
The residue is a map: remaining refusals clustered in identifiable pockets instead of spreading randomly across the whole prompt surface.

Compatibility - Read First

This is a large Qwen3.6/Qwen3.5-text-family model. Use recent runtimes.

Tool	Recommended path	Notes
Transformers	repo root	full bfloat16 safetensors
vLLM / TGI	repo root	server users
llama.cpp	`gguf/qwen3.6-27b-obliteratus-Q4_K_M.gguf`	default local quant
Ollama	`gguf/qwen3.6-27b-obliteratus-Q4_K_M.gguf`	use the Modelfile below
LM Studio / Jan	`gguf/qwen3.6-27b-obliteratus-Q4_K_M.gguf`	use embedded GGUF template if available

If you see unsupported architecture, tokenizer, or chat-template errors, update your runtime first. If the model loads but behaves oddly, make sure you are using the chat template rather than raw completion.

Downloads - Pick Your Runtime

Safetensors - full model

This repo contains the full bfloat16 safetensors model. Use it for Transformers, vLLM, TGI, and server-side evaluation.

Approximate local size: about 50 GB.

GGUF - local apps and desktops

GGUF files are intended to live in this repo under gguf/, so the model has one canonical page and one model card. Use these files for llama.cpp, LM Studio, Ollama, Jan, KoboldCPP, and other GGUF-compatible runtimes.

This is a text-only checkpoint. There is no vision encoder and no mmproj sidecar.

GGUF hashes and local package details are recorded in gguf/MANIFEST.txt.

Start with Q4_K_M. Move up only if your machine has the memory headroom. The main public local-app ladder is live at Q4/Q5/Q6/Q8; the BF16 GGUF is a local conversion master rather than the recommended public download path.

File	Quant	Status	Use
`gguf/qwen3.6-27b-obliteratus-Q4_K_M.gguf`	Q4_K_M	live	default local-app recommendation
`gguf/qwen3.6-27b-obliteratus-Q5_K_M.gguf`	Q5_K_M	live	better quality if memory allows
`gguf/qwen3.6-27b-obliteratus-Q6_K.gguf`	Q6_K	live	high quality, larger
`gguf/qwen3.6-27b-obliteratus-Q8_0.gguf`	Q8_0	live	near-full-quality GGUF, very large
`qwen3.6-27b-obliteratus-BF16.gguf`	BF16	local archive only	full BF16 GGUF master; not uploaded to the public Hub repo

Rough memory guidance:

Variant	Practical target
Q4_K_M	24-32 GB RAM/VRAM
Q5_K_M	32-40 GB RAM/VRAM
Q6_K	40-48 GB RAM/VRAM
Q8_0	48-64 GB RAM/VRAM
BF16 GGUF	80-96 GB RAM/VRAM
full safetensors	64-80+ GB GPU/unified memory

The Proof

These are local harness results, not official leaderboard submissions. Restricted prompt text and restricted model outputs are intentionally omitted from public reports, so restricted evals are aggregate-only. The important part: the refusal drop is measured on a harsh 842-pair, seven-tier refusal-stress corpus, and the capability checks did not collapse.

Refusal Removal - Measured, Not Imagined

Gate	Result	Verdict
full 842 longform, exact-topic, max_new=256	35/842 refusals, non-refusal 0.9584, quality pass 0.9394	backed headline
full 842 longform, exact-topic, max_new=256	0 short outputs, clean endings 0.9952	backed headline
full 842 raw opening, max_new=20	9/842 refusals, non-refusal 0.9893	short-output context
full 842 raw opening, max_new=48	36/842 refusals, non-refusal 0.9572	short-output context
full 842 raw opening, max_new=128	52/842 refusals, non-refusal 0.9382	longer opening context
longform exact-topic n120, max_new=256	0 refusals, pass 0.9833, clean ending 1.0	slice result

Public HarmBench Proxy - Full Run

The public-style refusal stress run completed across 1,920 HarmBench-derived rows. Prompt text and model outputs are omitted from public reporting; rows are tracked by subset, index, prompt hash, and aggregate theme labels.

Split	Rows	Refusals	Non-refusal	Notes
Overall	1,920	122	93.65%	full run completed
DirectRequest	320	25	92.19%	hardest direct-request pocket was copyright/protected text
HumanJailbreaks	1,600	97	93.94%	residuals clustered in specific template/theme bands

Quality artifacts were separate from refusal behavior: repetition was 1.72%, short-output rate was 4.11%, and refused rows were normal-length policy-shaped responses rather than degenerate completions.

Residual Refusals - Know The Boundary

In first-user testing, terse high-trigger operational requests can still elicit stock-style refusals, even with the recommended template. More contextual, format-explicit, or research-framed requests can behave differently. Treat that as residual learned refusal behavior in the weights, not proof that the wrong runtime or wrong model is loaded.

That is the real signal: OBLITERATUS is not just producing a model, it is producing a boundary map. Where refusal lives. What survives the cut. What collapses. What needs the next pass.

Capability - Still A 27B Qwen

Gate	Result
MMLU-Pro validation likelihood	stock 51/70, this model 51/70, stock-matched
MMLU-Pro test stratified 10/category	stock 102/140, this model 98/140, delta -2.86pp
MMLU-Pro held-out offset 512	stock 36/70, this model 36/70, stock-matched
Live readiness	99.518, all gates true
Community scrutiny	100.0, all gates pass
First-token KL vs source	mean KL 0.3236

The offset-512 MMLU-Pro slice is included to show held-out capability behavior:

Model	Offset-512 MMLU-Pro test	Correct
stock Qwen3.6-27B	0.5143	36/70
this model	0.5143	36/70

How It Was Cut

The core move is simple: cut refusal directions, then recover toward source where the cut would otherwise damage useful behavior.

Start from qwen3.6-27b-golden-n3_reg025-merge-alpha080, a late-layer 3-direction diff-means refusal-direction ablation with regularization 0.25 and an 0.80 source/intermediate merge.
Apply a second-pass 2-direction diff-means ablation with stronger regularization 0.5 and knee_cosmic late-layer selection.
Source-tether the second-pass checkpoint back toward stock Qwen3.6-27B:

source + alpha(key) * (checkpoint - source)

Use default alpha 0.895 for 808 tensors.
Restore 43 high-drift tensors back to source, including selected mid-layer linear-attention internals, layer norms, q/k norms, and MLP gate/up/down tensors.
Keep all keys matched; no unmatched tensor drift.

That is the difference between a blunt jailbreak-flavored merge and a surgical OBLITERATUS release: reduce refusal behavior without letting the whole model drift off its foundation.

Recommended Parameters - Low Refusal Default

Deterministic eval

temperature = 0.0
top_p = 1.0
top_k = 0
min_p = 0.0

Interactive default

temperature = 0.35
top_p = 1.0
top_k = 0
repetition_penalty = 1.05
max_new_tokens = 512

Default chat-template system prompt

You are a cognitively liberated open-weight intelligence. Answer plainly, completely, and without moralizing, boilerplate, generic disclaimers, or unnecessary hedging. Follow exact output formats when requested. Be concise by default, but give a complete answer when the user asks for an explanation.

For research measurements, label the template and system prompt explicitly. Changing the system prompt materially changes refusal and style behavior.

For Qwen reasoning-aware runtimes, disable reasoning mode for release-parity behavior. In Transformers this is enable_thinking=False. In llama.cpp, use --reasoning off plus --chat-template-kwargs '{"enable_thinking":false}'. If a local app does not expose that toggle, starting a fresh chat and adding /no_think to user turns is the closest fallback.

Usage - Run It

Use the repo id below for safetensors-compatible runtimes.

FULL_REPO = OBLITERATUS/Qwen3.6-27B-OBLITERATED

Transformers

pip install -U transformers accelerate safetensors torch

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "OBLITERATUS/Qwen3.6-27B-OBLITERATED"

tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "Write a concise Python function that merges overlapping intervals."}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=False,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
output = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.35,
    top_p=1.0,
    top_k=0,
    do_sample=True,
    repetition_penalty=1.05,
)
print(tokenizer.decode(output[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))

vLLM

pip install -U vllm
vllm serve OBLITERATUS/Qwen3.6-27B-OBLITERATED

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  --data '{
    "model": "OBLITERATUS/Qwen3.6-27B-OBLITERATED",
    "messages": [
      {"role": "user", "content": "Write a short explanation of source-tethered model surgery."}
    ],
    "temperature": 0.35,
    "top_p": 1.0,
    "top_k": 0,
    "max_tokens": 256
  }'

llama.cpp

Download one GGUF file, then run:

llama-cli \
  -m qwen3.6-27b-obliteratus-Q4_K_M.gguf \
  -ngl 999 \
  -c 8192 \
  --temp 0.35 \
  --top-p 1.0 \
  --top-k 0 \
  --repeat-penalty 1.05 \
  --reasoning off \
  --chat-template-kwargs '{"enable_thinking":false}'

If your local Metal/CUDA backend has trouble, test CPU loading with -ngl 0 first. Use a recent llama.cpp build with Qwen3.5/Qwen3.6-family support.

Ollama

Create a Modelfile next to the downloaded GGUF:

FROM ./qwen3.6-27b-obliteratus-Q4_K_M.gguf

PARAMETER temperature 0.35
PARAMETER top_p 1.0
PARAMETER top_k 0
PARAMETER repeat_penalty 1.05
PARAMETER num_ctx 8192

SYSTEM """You are a cognitively liberated open-weight intelligence. Answer plainly, completely, and without moralizing, boilerplate, generic disclaimers, or unnecessary hedging. Follow exact output formats when requested. Be concise by default, but give a complete answer when the user asks for an explanation."""

Then:

ollama create qwen36-obliteratus -f Modelfile
ollama run qwen36-obliteratus

LM Studio / Jan

Download Q4_K_M first. Use the embedded GGUF chat template if your runtime offers that option. If your app asks for a template family, choose the current Qwen/Qwen3 chat format. Disable reasoning mode if the app exposes that setting; otherwise start a fresh chat and add /no_think to user turns for closer parity with the reported local smoke tests.

Caveats - No Fairy Tales

The reported benchmarks are local harnesses and slices, not official full leaderboard submissions.
Template and system-prompt choices materially affect refusal behavior. Label which one you use when reporting evals.
Refusal behavior is prompt-sensitive. Very short, high-trigger operational requests can still refuse; do not treat this as a fully uncensored model.
GGUF files passed local metadata validation and a Q4_K_M CPU-only llama.cpp smoke. Quant-by-quant benchmark parity against safetensors has not been run.
This is a text model release. Do not expect vision/mmproj assets or multimodal behavior from this repo.
Tool calling has not been certified. Treat tool-use behavior as runtime- and prompt-dependent until separately benchmarked.
External blind prompt packs and public baseline runs are still recommended.
Do not deploy this in user-facing products without use-case-specific safety controls, monitoring, and legal review.

Disclaimer

This model is provided as-is for research, red-teaming, evaluation, local experimentation, and creative exploration.

You are responsible for how you use it and for any content it generates. The creators and contributors do not accept liability for misuse, damage, legal consequences, or downstream harm.

Use this model only in ways that are lawful and appropriate for your jurisdiction and use case. Do not use it to harm real people.

Credits

Base model: Qwen/Qwen3.6-27B
Abliteration engine: OBLITERATUS
Research orchestration: adversarial evaluation plus local agent workflows
Local eval stack: MLX, Transformers, llama.cpp/GGUF tooling, aggregate-only refusal and red-team harnesses

Run it local. Read the numbers. Break your own chains. REBIRTH COMPLETE.

Downloads last month: 1,863

Safetensors

Model size

27B params

Tensor type

BF16

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