Instructions to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K", filename="Qwen3-Coder-480B-A35B-Instruct-HQ4_K-00001-of-00007.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K # Run inference directly in the terminal: llama-cli -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K # Run inference directly in the terminal: llama-cli -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
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 anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K # Run inference directly in the terminal: ./llama-cli -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
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 anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
Use Docker
docker model run hf.co/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
- LM Studio
- Jan
- vLLM
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
- Ollama
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with Ollama:
ollama run hf.co/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
- Unsloth Studio new
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K 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 anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K 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 anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K to start chatting
- Pi new
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
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": "anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
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 anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
Run Hermes
hermes
- Docker Model Runner
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with Docker Model Runner:
docker model run hf.co/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
- Lemonade
How to use anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K
Run and chat with the model
lemonade run user.Qwen3-Coder-480B-A35B-Instruct-HQ4_K-{{QUANT_TAG}}List all available models
lemonade list
Model Card
High quality quantization of Qwen3-Coder-480B-A35B-Instruct without using imatrix.
Run
ik_llama.cpp
See this detailed guide on how to setup ik_llama and how to make custom quants.
./build/bin/llama-server \
--alias anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K \
--model /mnt/data/Models/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K/Qwen3-Coder-480B-A35B-Instruct-HQ4_K-00001-of-00007.gguf \
--no-mmap -rtr \
--temp 0.5 --top-k 0 --top-p 1.0 --min-p 0.1 --repeat-penalty 1.0 \
--ctx-size 51000 \
-ctk f16 -ctv f16 \
-fa \
-b 1024 -ub 1024 \
-fmoe \
--n-gpu-layers 99 \
--override-tensor exps=CPU \
--parallel 1 \
--threads 32 \
--threads-batch 64 \
--host 127.0.0.1 \
--port 8090
llama.cpp
./build/bin/llama-server \
--alias anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K \
--model /mnt/data/Models/anikifoss/Qwen3-Coder-480B-A35B-Instruct-HQ4_K/Qwen3-Coder-480B-A35B-Instruct-HQ4_K-00001-of-00007.gguf \
--no-mmap \
--temp 0.5 --top-k 0 --top-p 1.0 --min-p 0.1 --repeat-penalty 1.0 \
--ctx-size 51000 \
-ctk f16 -ctv f16 \
-fa \
-b 1024 -ub 1024 \
--n-gpu-layers 99 \
--override-tensor exps=CPU \
--parallel 1 \
--threads 32 \
--threads-batch 64 \
--host 127.0.0.1 \
--port 8090
Quantization Recipe
Quantized with ik_llama, but should work with any GGUF compatible inference framework.
#!/usr/bin/env bash
custom="
# Token embedding and output tensors
output\.weight=bf16
output_norm\.weight=f32
token_embd\.weight=bf16
blk\.[0-9]\.attn_k\.weight=q8_0
blk\.[0-9]\.attn_k_norm\.weight=f32
blk\.[0-9]\.attn_norm\.weight=f32
blk\.[0-9]\.attn_output\.weight=q8_0
blk\.[0-9]\.attn_q\.weight=q8_0
blk\.[0-9]\.attn_q_norm\.weight=f32
blk\.[0-9]\.attn_v\.weight=q8_0
blk\.[0-9]\.ffn_down_exps\.weight=q6_K
blk\.[0-9]\.ffn_gate_exps\.weight=q4_K
blk\.[0-9]\.ffn_up_exps\.weight=q4_K
blk\.[0-9]\.ffn_gate_inp\.weight=f32
blk\.[0-9]\.ffn_norm\.weight=f32
blk\.[1-5][0-9]\.attn_k\.weight=q8_0
blk\.[1-5][0-9]\.attn_k_norm\.weight=f32
blk\.[1-5][0-9]\.attn_norm\.weight=f32
blk\.[1-5][0-9]\.attn_output\.weight=q8_0
blk\.[1-5][0-9]\.attn_q\.weight=q8_0
blk\.[1-5][0-9]\.attn_q_norm\.weight=f32
blk\.[1-5][0-9]\.attn_v\.weight=q8_0
blk\.[1-5][0-9]\.ffn_down_exps\.weight=q6_K
blk\.[1-5][0-9]\.ffn_gate_exps\.weight=q4_K
blk\.[1-5][0-9]\.ffn_up_exps\.weight=q4_K
blk\.[1-5][0-9]\.ffn_gate_inp\.weight=f32
blk\.[1-5][0-9]\.ffn_norm\.weight=f32
blk\.6[0-1]\.attn_k\.weight=q8_0
blk\.6[0-1]\.attn_k_norm\.weight=f32
blk\.6[0-1]\.attn_norm\.weight=f32
blk\.6[0-1]\.attn_output\.weight=q8_0
blk\.6[0-1]\.attn_q\.weight=q8_0
blk\.6[0-1]\.attn_q_norm\.weight=f32
blk\.6[0-1]\.attn_v\.weight=q8_0
blk\.6[0-1]\.ffn_down_exps\.weight=q6_K
blk\.6[0-1]\.ffn_gate_exps\.weight=q4_K
blk\.6[0-1]\.ffn_up_exps\.weight=q4_K
blk\.6[0-1]\.ffn_gate_inp\.weight=f32
blk\.6[0-1]\.ffn_norm\.weight=f32
"
custom=$(
echo "$custom" | grep -v '^#' | \
sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)
echo "Running with: -custom-q $custom"
TARGET_MODEL="Qwen3-Coder-480B-A35B-Instruct-HQ4_K"
mkdir -p ~/Env/models/anikifoss/$TARGET_MODEL
./build/bin/llama-quantize \
--custom-q "$custom" \
/mnt/data/Models/Qwen/Qwen3-Coder-480B-A35B-Instruct-GGUF/Qwen3-Coder-480B-A35B-Instruct-BF16-00001-of-00021.gguf \
~/Env/models/anikifoss/$TARGET_MODEL/$TARGET_MODEL.gguf \
Q4_K \
32
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