- 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
-
- 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
