Instructions to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF",
	filename="Qwen3-Coder-480B-A35B-Instruct-IQ1_M_R4-00001-of-00003.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
# Run inference directly in the terminal:
llama-cli -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

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 sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
# Run inference directly in the terminal:
./llama-cli -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

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 sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Use Docker

docker model run hf.co/sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

LM Studio
Jan
Ollama
How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with Ollama:
```
ollama run hf.co/sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
```

Unsloth Studio new

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF 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 sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF 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 sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF to start chatting

Pi new

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Run Hermes

hermes

Docker Model Runner
How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R
```

Lemonade

How to use sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF:IQ1_M_R

Run and chat with the model

lemonade run user.Qwen3-Coder-480B-A35B-Instruct-GGUF-IQ1_M_R

List all available models

lemonade list

This is an IQ1_M_R4 quant of Qwen3-Coder-480B-A35B-Instruct, using the imatrix from ubergarm, with a slightly modified recipe:

# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq1_m_r4
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_m_r4

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k

The file size is 112749523904 bytes, or 105.006 GiB.

With the routed experts fully offloaded to CPU, the memory usage (before context) will be:

llm_load_tensors:        CPU buffer size = 97863.13 MiB
llm_load_tensors:  CUDA_Host buffer size =   500.77 MiB
llm_load_tensors:      CUDA0 buffer size =  9156.73 MiB

I have a code refactoring eval that uses greedy decoding with deterministic output, and this is the smallest quant I tested that can still perform the same as the BF16 version (openrouter, alibaba provider).

Note that this quant only works with ik_llama.cpp.

Big thanks to ubergarm for sharing the imatrix, the recipes, and most importantly the detailed guidance and thought processes.

Downloads last month: 15

GGUF

Model size

480B params

Architecture

qwen3moe

Hardware compatibility

1-bit

Inference Providers NEW

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Model tree for sokann/Qwen3-Coder-480B-A35B-Instruct-GGUF

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Qwen/Qwen3-Coder-480B-A35B-Instruct

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