Libraries MLX 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 Inference Providers 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 Desktop 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 
