Instructions to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF 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 ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_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 ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_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 ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Qwen3-235B-A22B-Instruct-2507-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/Qwen3-235B-A22B-Instruct-2507-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Ollama
How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-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/Qwen3-235B-A22B-Instruct-2507-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/Qwen3-235B-A22B-Instruct-2507-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/Qwen3-235B-A22B-Instruct-2507-GGUF to start chatting

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_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": "ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K
```

Lemonade

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen3-235B-A22B-Instruct-2507-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_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 ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B-Instruct-2507

This quant collection REQUIRES ik_llama.cpp fork to support the ik's latest SOTA quants and optimizations! Do not download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

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.

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!

Quant Collection

Perplexity computed against wiki.test.raw. These first two are just test quants for baseline perplexity comparison:

bf16 437.989 GiB (16.003 BPW)
- Final estimate: PPL = 4.3079 +/- 0.02544
Q8_0 232.769 GiB (8.505 BPW)
- Final estimate: PPL = 4.3139 +/- 0.02550

`IQ5_K` 161.722 GiB (5.909 BPW)

Final estimate: PPL = 4.3351 +/- 0.02566

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# 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=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ5_K.gguf \
    IQ5_K \
    192

`IQ4_K` 134.183 GiB (4.903 BPW)

Final estimate: PPL = 4.3668 +/- 0.02594

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# 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=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ4_K.gguf \
    IQ4_K \
    192

`pure-IQ4_KS` 116.994 GiB (4.275 BPW)

Final estimate: PPL = 4.4156 +/- 0.02624

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_k.*=iq4_ks
blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_v.*=iq4_ks
blk\..*\.attn_output.*=iq4_ks

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_ks

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-eaddario-imat-pure-IQ4_KS.gguf \
    IQ4_KS \
    192

`IQ4_KSS` 115.085 GiB (4.205 BPW)

Final estimate: PPL = 4.4017 +/- 0.02614

This one is a little funky just for fun. Seems smort!

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

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

# Routed Experts
blk\.(0|1|2|3)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2|3)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ4_KSS.gguf \
    IQ4_KSS \
    192

`IQ3_K` 106.644 GiB (3.897 BPW)

Final estimate: PPL = 4.4561 +/- 0.02657

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# 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=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ3_K.gguf \
    IQ3_K \
    192

`IQ3_KS` 101.308 GiB (3.702 BPW)

Final estimate: PPL = 4.4915 +/- 0.02685

Another funky smort one!

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

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

# Routed Experts
blk\.(0|1|2|3)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2|3)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ3_KS.gguf \
    IQ3_KS \
    192

`IQ2_KL` 81.866 GiB (2.991 BPW)

Final estimate: PPL = 4.7912 +/- 0.02910

👈 Secret Recipe

#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# 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=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/imatrix-Qwen3-235B-A22B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF/Qwen3-235B-A22B-Instruct-2507-IQ2_KL.gguf \
    IQ2_KL \
    192

Quick Start

This example is for a single CUDA GPU hybrid infrencing with CPU/RAM. Check ik_llama.cpp discussions or my other quants for more examples for multi-GPU etc.

./build/bin/llama-server \
  --model /models/IQ5_K/Qwen3-235B-A22B-Instruct-IQ5_K-00001-of-00004.gguf \
  --alias ubergarm/Qwen3-235B-A22B-Instruct-2507 \
  -fa -fmoe \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -ngl 99 \
  -ot "blk\.[0-9]\.ffn.*=CUDA0" \
  -ot "blk.*\.ffn.*=CPU \
  --threads 16 \
  -ub 4096 -b 4096 \
  --host 127.0.0.1 \
  --port 8080

References

Downloads last month: 61

GGUF

Model size

235B params

Architecture

qwen3moe

Hardware compatibility

2-bit

Model tree for ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF

Base model

Qwen/Qwen3-235B-A22B-Instruct-2507

Quantized

(65)

this model

ik_llama.cpp imatrix Quantizations of Qwen/Qwen3-235B-A22B-Instruct-2507

Big Thanks

Quant Collection

IQ5_K 161.722 GiB (5.909 BPW)

IQ4_K 134.183 GiB (4.903 BPW)

pure-IQ4_KS 116.994 GiB (4.275 BPW)

IQ4_KSS 115.085 GiB (4.205 BPW)

IQ3_K 106.644 GiB (3.897 BPW)

IQ3_KS 101.308 GiB (3.702 BPW)

IQ2_KL 81.866 GiB (2.991 BPW)

Quick Start

References

Model tree for ubergarm/Qwen3-235B-A22B-Instruct-2507-GGUF

`ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B-Instruct-2507

`IQ5_K` 161.722 GiB (5.909 BPW)

`IQ4_K` 134.183 GiB (4.903 BPW)

`pure-IQ4_KS` 116.994 GiB (4.275 BPW)

`IQ4_KSS` 115.085 GiB (4.205 BPW)

`IQ3_K` 106.644 GiB (3.897 BPW)

`IQ3_KS` 101.308 GiB (3.702 BPW)

`IQ2_KL` 81.866 GiB (2.991 BPW)