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

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

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

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-Next

NOTE ik_llama.cpp can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with Nexesenex/croco.cpp fork of KoboldCPP with Windows builds. Also check for ik_llama.cpp windows builds by Thireus here..

These quants provide best in class perplexity for the given memory footprint.

Big Thanks

Shout out to Wendell and the Level1Techs crew, the community Forums, YouTube Channel! BIG thanks for providing BIG hardware expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on BeaverAI Club Discord and on r/LocalLLaMA for tips and tricks helping each other run, test, and benchmark all the fun new models! Thanks to huggingface for hosting all these big quants!

Finally, I really appreciate the support from aifoundry.org so check out their open source RISC-V based solutions!

Quant Collection

Perplexity computed against wiki.test.raw. (lower is "better")

These two are just test quants for baseline perplexity comparison and not available for download here:

BF16 148.502 GiB (16.010 BPW)
- PPL over 584 chunks for n_ctx=512 = 8.2278 +/- 0.06392
Q8_0 78.982 GiB (8.515 BPW)
- PPL over 584 chunks for n_ctx=512 = 8.2239 +/- 0.06389

NOTE: The first split file is much smaller on purpose to only contain metadata, its fine!

Q4_0 44.355 GiB (4.782 BPW)

PPL over 584 chunks for n_ctx=512 = 8.2543 +/- 0.06411

Optimized Vulkan MoE Mix mainline llama.cpp compatible as well!

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 60 Repeating Layers [0-59]

## Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attn_gate\.weight=q8_0
blk\..*\.attn_qkv\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0
blk\..*\.attn_q\.weight=q8_0
blk\..*\.attn_k\.weight=q8_0
blk\..*\.attn_v\.weight=q8_0
blk\..*\.ssm_ba\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Shared Expert Layers [0-59]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [0-59]
blk\..*\.ffn_down_exps\.weight=q4_1
blk\..*\.ffn_(gate|up)_exps\.weight=q4_0

# Non-Repeating Layers
token_embd\.weight=q4_1
output\.weight=q8_0
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

    #--dry-run \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/imatrix-Qwen3-Coder-Next-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-512x2.5B-BF16-00001-of-00004.gguf \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-Q4_0.gguf \
    Q4_0 \
    128

IQ4_KSS 39.377 GiB (4.245 BPW)

PPL over 584 chunks for n_ctx=512 = 8.3069 +/- 0.06459

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 60 Repeating Layers [0-59]

## Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attn_gate\.weight=q8_0
blk\..*\.attn_qkv\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0
blk\..*\.attn_q\.weight=q8_0
blk\..*\.attn_k\.weight=q8_0
blk\..*\.attn_v\.weight=q8_0
blk\..*\.ssm_ba\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Shared Expert Layers [0-59]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [0-59]
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

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

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --dry-run \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/imatrix-Qwen3-Coder-Next-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-512x2.5B-BF16-00001-of-00004.gguf \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-IQ4_KSS.gguf \
    IQ4_KSS \
    128

smol-IQ3_KS 30.728 GiB (3.313 BPW)

PPL over 584 chunks for n_ctx=512 = 8.4605 +/- 0.06623

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 60 Repeating Layers [0-59]

## Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attn_gate\.weight=iq6_k
blk\..*\.attn_qkv\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k
blk\..*\.attn_q\.weight=iq6_k
blk\..*\.attn_k\.weight=iq6_k
blk\..*\.attn_v\.weight=iq6_k
blk\..*\.ssm_ba\.weight=iq6_k
blk\..*\.ssm_out\.weight=iq6_k

# Shared Expert Layers [0-59]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k

# Routed Experts Layers [0-59]
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

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

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

    #--exclude-weights ffn_gate_exps \
    #--exclude-weights ffn_up_exps \
    #--dry-run \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/imatrix-Qwen3-Coder-Next-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-512x2.5B-BF16-00001-of-00004.gguf \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-smol-IQ3_KS.gguf \
    IQ3_KS \
    128

smol-IQ2_KS 22.097 GiB (2.382 BPW)

PPL over 584 chunks for n_ctx=512 = 9.4488 +/- 0.07565

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 60 Repeating Layers [0-59]

## Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attn_gate\.weight=q8_0
blk\..*\.attn_qkv\.weight=q8_0
blk\..*\.attn_output\.weight=q8_0
blk\..*\.attn_q\.weight=q8_0
blk\..*\.attn_k\.weight=q8_0
blk\..*\.attn_v\.weight=q8_0
blk\..*\.ssm_ba\.weight=q8_0
blk\..*\.ssm_out\.weight=q8_0

# Shared Expert Layers [0-59]
blk\..*\.ffn_down_shexp\.weight=q8_0
blk\..*\.ffn_(gate|up)_shexp\.weight=q8_0

# Routed Experts Layers [0-59]
blk\..*\.ffn_down_exps\.weight=iq2_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_ks

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

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --dry-run \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/imatrix-Qwen3-Coder-Next-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-512x2.5B-BF16-00001-of-00004.gguf \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-smol-IQ2_KS.gguf \
    IQ2_KS \
    128

IQ1_KT 19.056 GiB (2.055 BPW)

PPL over 584 chunks for n_ctx=512 = 9.6513 +/- 0.07696

👈 Secret Recipe

#!/usr/bin/env bash

custom="
# 60 Repeating Layers [0-59]

## Gated Attention/Delta Net [Blended 0-59]
blk\..*\.attn_gate\.weight=iq6_k
blk\..*\.attn_qkv\.weight=iq6_k
blk\..*\.attn_output\.weight=iq6_k
blk\..*\.attn_q\.weight=iq6_k
blk\..*\.attn_k\.weight=iq6_k
blk\..*\.attn_v\.weight=iq6_k
blk\..*\.ssm_ba\.weight=iq6_k
blk\..*\.ssm_out\.weight=iq6_k

# Shared Expert Layers [0-59]
blk\..*\.ffn_down_shexp\.weight=iq6_k
blk\..*\.ffn_(gate|up)_shexp\.weight=iq6_k

# Routed Experts Layers [0-59]
blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

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

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

    #--dry-run \
#gdb -q --args \
numactl -N ${SOCKET} -m ${SOCKET} \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/imatrix-Qwen3-Coder-Next-BF16.dat \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-512x2.5B-BF16-00001-of-00004.gguf \
    /mnt/data/models/ubergarm/Qwen3-Coder-Next-GGUF/Qwen3-Coder-Next-IQ1_KT.gguf \
    IQ1_KT \
    128

Quick Start

Check some recent model cards for examples on running models.

# Clone and checkout
$ git clone https://github.com/ikawrakow/ik_llama.cpp
$ cd ik_llama.cpp

# Build for hybrid CPU+CUDA
$ cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON
$ cmake --build build --config Release -j $(nproc)

# Download Desired Quants
$ pip install huggingface_hub
$ hf download --local-dir ./ --include=smol-IQ2_XS/*.gguf ubergarm/Qwen3-Coder-Next-GGUF

# Full GPU offload
# For 2 or more GPUs keep an eye on `-sm graph` support:
# https://github.com/ikawrakow/ik_llama.cpp/pull/1292
CUDA_VISIBLE_DEVICES="0,1" \
./build/bin/llama-server \
  --model "$model" \
  --alias Qwen3-Coder-Next \
  -c 262144 \
  -fa on \
  -ger \
  --merge-qkv \
  -sm graph \
  -ngl 99 \
  -ub 2048 -b 2048 \
  --threads 1 \
  --host 127.0.0.1 \
  --port 8080 \
  --jinja \
  --no-mmap

# Hybrid CPU+GPU
# basically use --n-cpu-moe etc...
echo TODO

# CPU-Only
# Gated delta net CPU-only performance seems slower than other architechtures, ideally have at least 1x GPU for attn/kv-cache
numactl -N "$SOCKET" -m "$SOCKET" \
./build/bin/llama-server \
    --model "$model"\
    --alias Qwen3-Coder-Next \
    --ctx-size 131072 \
    -ger \
    --merge-qkv \
    -ctk q8_0 -ctv q8_0 \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 96 \
    --threads-batch 128 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap \
    --jinja

References

Downloads last month: 325

GGUF

Model size

80B params

Architecture

qwen3next

Hardware compatibility

2-bit

4-bit

View +1 variant

Model tree for ubergarm/Qwen3-Coder-Next-GGUF

Base model

Qwen/Qwen3-Coder-Next

Quantized

(97)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-Coder-Next

Big Thanks

Quant Collection

Q4_0 44.355 GiB (4.782 BPW)

IQ4_KSS 39.377 GiB (4.245 BPW)

smol-IQ3_KS 30.728 GiB (3.313 BPW)

smol-IQ2_KS 22.097 GiB (2.382 BPW)

IQ1_KT 19.056 GiB (2.055 BPW)

Quick Start

References

Model tree for ubergarm/Qwen3-Coder-Next-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-Next