Instructions to use 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split 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 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_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 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_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 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M

Use Docker

docker model run hf.co/888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M

LM Studio
Jan
Ollama
How to use 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split with Ollama:
```
ollama run hf.co/888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
```
Unsloth Desktop
Docker Model Runner
How to use 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split with Docker Model Runner:
```
docker model run hf.co/888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M
```

Lemonade

How to use 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split:Q3_K_M

Run and chat with the model

lemonade run user.Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split-Q3_K_M

List all available models

lemonade list

Atomic Chat

Qwen3.8-27B-OBLITERATED Q3_K_M — wllama split

OBLITERATUS/Qwen3.8-27B-OBLITERATED (abliterated build of Qwen/Qwen3.8-27B; GGUF quants by OBLITERATUS) repackaged unmodified: the original Q3_K_M GGUF split into 8 shards (< 2 GB each) with llama-gguf-split for in-browser use with wllama, which cannot fetch single files over 2 GB.

Total size: 13,500,729,248 bytes (8 shards)
Load via the first shard; wllama auto-resolves the rest.
Needs roughly 32 GB of free RAM to run in-browser.
License and usage restrictions follow the original model (Apache-2.0).

Downloads last month: 33

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

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Model tree for 888rok/Qwen3.8-27B-OBLITERATED-Q3_K_M-wllama-split

Base model

Qwen/Qwen3.8-27B

Quantized

OBLITERATUS/Qwen3.8-27B-OBLITERATED

Quantized

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