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

Libraries

How to use OBLITERATUS/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("OBLITERATUS/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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

LM Studio
Jan

vLLM

How to use OBLITERATUS/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 "OBLITERATUS/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": "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

MLX LM

How to use OBLITERATUS/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 "OBLITERATUS/Qwen3.8-27B-OBLITERATED"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OBLITERATUS/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": "OBLITERATUS/Qwen3.8-27B-OBLITERATED",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

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

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OBLITERATUS/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

Atomic Chat

Qwen3.8-27B-OBLITERATED / model-00012-of-00028.safetensors

pliny-the-prompter

V3: fixed merge (abliterated weights, not stock)

d39e846 verified 3 days ago

Download

History Blame Contribute Delete

1.86 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: a877029bb7ed4384acfbc13b916589963fed1651c4304e7c4564b610e65b4941
Size of remote file:: 1.86 GB
SHA256:: 28204de1451f24d46dddcd1d54ffad8ff388d80c42743da67c9fb1432b5bab95

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.