Instructions to use Goldkoron/Qwen3.5-122B-A10B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Goldkoron/Qwen3.5-122B-A10B 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 Goldkoron/Qwen3.5-122B-A10B
# Run inference directly in the terminal:
llama cli -hf Goldkoron/Qwen3.5-122B-A10B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Goldkoron/Qwen3.5-122B-A10B
# Run inference directly in the terminal:
llama cli -hf Goldkoron/Qwen3.5-122B-A10B

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 Goldkoron/Qwen3.5-122B-A10B
# Run inference directly in the terminal:
./llama-cli -hf Goldkoron/Qwen3.5-122B-A10B

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 Goldkoron/Qwen3.5-122B-A10B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Goldkoron/Qwen3.5-122B-A10B

Use Docker

docker model run hf.co/Goldkoron/Qwen3.5-122B-A10B

LM Studio
Jan
Ollama
How to use Goldkoron/Qwen3.5-122B-A10B with Ollama:
```
ollama run hf.co/Goldkoron/Qwen3.5-122B-A10B
```

Unsloth Studio

How to use Goldkoron/Qwen3.5-122B-A10B 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 Goldkoron/Qwen3.5-122B-A10B 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 Goldkoron/Qwen3.5-122B-A10B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Goldkoron/Qwen3.5-122B-A10B to start chatting

How to use Goldkoron/Qwen3.5-122B-A10B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Goldkoron/Qwen3.5-122B-A10B

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": "Goldkoron/Qwen3.5-122B-A10B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use Goldkoron/Qwen3.5-122B-A10B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Goldkoron/Qwen3.5-122B-A10B

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 "Goldkoron/Qwen3.5-122B-A10B" \
  --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"

Docker Model Runner
How to use Goldkoron/Qwen3.5-122B-A10B with Docker Model Runner:
```
docker model run hf.co/Goldkoron/Qwen3.5-122B-A10B
```

Lemonade

How to use Goldkoron/Qwen3.5-122B-A10B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Goldkoron/Qwen3.5-122B-A10B

Run and chat with the model

lemonade run user.Qwen3.5-122B-A10B-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use Goldkoron/Qwen3.5-122B-A10B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Goldkoron/Qwen3.5-122B-A10B

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 Goldkoron/Qwen3.5-122B-A10B

Run Hermes

hermes

Atomic Chat

Qwen3.5-122B-A10B — Gutenberg (K_G) Quants

Quantizations of Qwen3.5-122B-A10B using the Gutenberg quantization strategy.

Available Quants

Quant	Size	BPW
K_G_6.00	85.5 GiB	6.02
K_G_5.00	71.3 GiB	5.02
K_G_4.50	64.3 GiB	4.52
K_G_4.00	57.2 GiB	4.02
K_G_3.50	50.0 GiB	3.51
K_G_3.00	42.9 GiB	3.02
K_G_2.50	35.4 GiB	2.49

KLD Comparison vs Unsloth UD Quants

Measured against Q8_K_XL reference logits. Lower KLD = closer to source model quality.

Model	Size	BPW	KLD	Same Top P
UD-Q6_K_XL	104.7 GiB	7.36	0.002771	96.55%
K_G_6.00	85.5 GiB	6.02	0.003026	96.55%
UD-Q5_K_XL	85.6 GiB	6.02	0.003329	96.34%
K_G_5.00	71.3 GiB	5.02	0.004002	96.14%
UD-Q4_K_XL	71.7 GiB	5.05	0.004898	95.70%
K_G_4.50	64.3 GiB	4.52	0.005178	95.68%
K_G_4.00	57.2 GiB	4.02	0.006769	95.33%
K_G_3.50	50.0 GiB	3.51	0.010662	94.24%
UD-Q3_K_XL	53.1 GiB	3.73	0.014053	93.16%
K_G_3.00	42.9 GiB	3.02	0.018017	92.94%
UD-IQ2_XXS	34.1 GiB	2.40	0.056205	87.12%
K_G_2.50	35.4 GiB	2.49	0.034715	90.48%

What is Gutenberg?

Gutenberg uses KLD sensitivity data to allocate quantization precision where it matters most. Instead of applying uniform quantization, each expert tensor is ranked by its measured impact on output quality, then assigned to one of three tiers (+1, base, or -1 quant level) within a BPW budget. Non-expert tensors are kept at Q8_0.

KLD measurements show improved output fidelity compared to standard quants at equivalent model sizes.

Compatibility

Fully compatible with stock llama.cpp, llama-server, LM Studio, and any GGUF-compatible runtime. No custom builds required.

Downloads last month: 84

GGUF

Model size

122B params

Architecture

qwen35moe

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

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Model tree for Goldkoron/Qwen3.5-122B-A10B

Base model

Qwen/Qwen3.5-122B-A10B

Quantized

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