Instructions to use ubergarm/Qwen3-Coder-Next-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ubergarm/Qwen3-Coder-Next-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ubergarm/Qwen3-Coder-Next-GGUF",
	filename="Qwen3-Coder-Next-IQ1_KT.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 ubergarm/Qwen3-Coder-Next-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

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 ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

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 ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-Coder-Next-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-Coder-Next-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ubergarm/Qwen3-Coder-Next-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

Ollama
How to use ubergarm/Qwen3-Coder-Next-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
```

Unsloth Studio new

How to use ubergarm/Qwen3-Coder-Next-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 ubergarm/Qwen3-Coder-Next-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 ubergarm/Qwen3-Coder-Next-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Qwen3-Coder-Next-GGUF to start chatting

Pi new

How to use ubergarm/Qwen3-Coder-Next-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

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": "ubergarm/Qwen3-Coder-Next-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Qwen3-Coder-Next-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 ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

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 ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

Run Hermes

hermes

Docker Model Runner
How to use ubergarm/Qwen3-Coder-Next-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-Coder-Next-GGUF:Q4_0
```

Lemonade

How to use ubergarm/Qwen3-Coder-Next-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-Coder-Next-GGUF:Q4_0

Run and chat with the model

lemonade run user.Qwen3-Coder-Next-GGUF-Q4_0

List all available models

lemonade list

Q4_0 Like Qwen3.5-35B-A3B

by engrtipusultan - opened Feb 26

Discussion

engrtipusultan

Feb 26

•

edited Feb 26

Would it be possible for you to add Q4_0 like you did for Qwen3.5-35B-A3B which is fast on vulkan and mainline llama.cpp. I think it will be useful for many people.

https://huggingface.co/ubergarm/Qwen3.5-35B-A3B-GGUF#q4_0-19776-gib-4901-bpw

Also, is there any reason you did not bump token_embd.weight to Q8

ubergarm

Owner Feb 26

@engrtipusultan

I'll take a look though it will be larger as Qwen3-Coder-Next is ~80B so my custom Q4_0 vulkan mix will likely be about 80*(5/8)= 50 GB or so and require quite a bit of VRAM, but would be fine on strix halo unified memory system if that is okay with you?

Also, is there any reason you did not bump token_embd.weight to Q8

Yes, the old tradition on mainline is token_embd@q4_K and final "head" output@q6_K... For ik_llama.cpp I use the newer variantes e.g. iq4_k and iq6_k which I've measured as slightly better.

So going with q4_1 at 5bpw is still comparable or slightly better to the q4_K. It saves a chunk of VRAM which can then be used for longer kv-cache context given it is fairly tight in 24GB VRAM already. Its all trade offs, and I wouldn't expect much benefit to PPL/KLD bumping it to the larger q8_0 which would also slow down TG slightly.

ubergarm

Owner Feb 26

Yeah I'll cook it up and upload it then update the perplexity graphs: llama_model_quantize_internal: quant size = 45419.73 MB

ubergarm

Owner Feb 26

@engrtipusultan

Keep me posted how it goes with your rig! enjoy: https://huggingface.co/ubergarm/Qwen3-Coder-Next-GGUF?show_file_info=Qwen3-Coder-Next-Q4_0.gguf

it has quite good perplexity too!

engrtipusultan

Feb 26

I have 56Gb unified available for inference so I think that will be fine for all 64GB unified systems.
Have you done any testing on iq4_nl vs q4_1 for perplexity and KLD. Atleast on my system Iq4_nl is faster.

I wish there was no dispute between ik and greg it would have been overall better for community.

engrtipusultan

Feb 26

@engrtipusultan

Keep me posted how it goes with your rig! enjoy: https://huggingface.co/ubergarm/Qwen3-Coder-Next-GGUF?show_file_info=Qwen3-Coder-Next-Q4_0.gguf

it has quite good perplexity too!

Wow that was fast awesome. Thank you

ubergarm

Owner Feb 26

Have you done any testing on iq4_nl vs q4_1 for perplexity and KLD. Atleast on my system Iq4_nl is faster.

Hrmm, i haven't done a 1 to 1 comparison swapping out only q4_1 for iq4_nl... i'm surprised that iq4_nl is faster for you on both prompt processing and token generation... iq4_nl may be slightly faster on TG given it is a little smaller like 4.5ish bpw vs q4_1's 5ish bpw i think

I'd love to see some numbers on that and will keep it in the back of my head as there may very well be iq4_nl might e better than q4_1...

agreed i wish we could run them all on both forks...

engrtipusultan

Feb 26

Just wanted to ask does your script and last upload take follow pr into account.

https://github.com/ggml-org/llama.cpp/pull/19139

ubergarm

Owner Feb 26

•

edited Feb 26

@engrtipusultan

no that was just merged 7 hours ago and with ik_llama.cpp there are a number of fusion optimizations built in already so am unsure that i would ever use it?

do you have opinions about it?

EDIT I'm asking them about it here: https://github.com/ggml-org/llama.cpp/pull/19139#issuecomment-3968981015

engrtipusultan

Feb 27

I do not have an opinion I was simply asking your opinion since you are taking both llama.cpp into account.

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