Instructions to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF 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("ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF")

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 ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-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 ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama cli -hf ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

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 ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

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 ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

Use Docker

docker model run hf.co/ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

LM Studio
Jan

vLLM

How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-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": "ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

Ollama
How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with Ollama:
```
ollama run hf.co/ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
```
Unsloth Desktop

MLX LM

How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with Docker Model Runner:
```
docker model run hf.co/ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S
```

Lemonade

How to use ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF-Q4_K_S

List all available models

lemonade list

Atomic Chat

ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF

This model was converted to GGUF format from OBLITERATUS/Qwen3.8-27B-OBLITERATED using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF --hf-file qwen3.8-27b-obliterated-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF --hf-file qwen3.8-27b-obliterated-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF --hf-file qwen3.8-27b-obliterated-q4_k_s.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF --hf-file qwen3.8-27b-obliterated-q4_k_s.gguf -c 2048

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for ptq94h/Qwen3.8-27B-OBLITERATED-Q4_K_S-GGUF

Base model

Qwen/Qwen3.8-27B

Quantized

OBLITERATUS/Qwen3.8-27B-OBLITERATED

Quantized

(14)

this model