Instructions to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ 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 w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
llama cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
llama cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

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 w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
./llama-cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

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 w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Use Docker

docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

LM Studio
Jan

vLLM

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "w-ahmad/Qwen3.5-9B-GGUF-MoQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "w-ahmad/Qwen3.5-9B-GGUF-MoQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Ollama
How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Ollama:
```
ollama run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
```

Unsloth Studio

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ 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 w-ahmad/Qwen3.5-9B-GGUF-MoQ 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 w-ahmad/Qwen3.5-9B-GGUF-MoQ to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for w-ahmad/Qwen3.5-9B-GGUF-MoQ to start chatting

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

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": "w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

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 "w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16" \
  --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 w-ahmad/Qwen3.5-9B-GGUF-MoQ with Docker Model Runner:
```
docker model run hf.co/w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16
```

Lemonade

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Run and chat with the model

lemonade run user.Qwen3.5-9B-GGUF-MoQ-BF16

List all available models

lemonade list

Hermes Agent

How to use w-ahmad/Qwen3.5-9B-GGUF-MoQ with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

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 w-ahmad/Qwen3.5-9B-GGUF-MoQ:BF16

Run Hermes

hermes

Atomic Chat

w-ahmad commited on Apr 28

Commit

292ab01

verified ·

1 Parent(s): a41b53a

Update README.md

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@@ -1,2 +1,91 @@
-Updated MoQ are coming soon with all quants and evaluation results.
-I improved the quants assigning method for higher performance.

+---
+language:
+- en
+library_name: gguf
+tags:
+- MoQ
+- mixture-of-quants
+- GGUF
+- QWEN
+- quantization
+base_model:
+- Qwen/Qwen3.5-9B
+license: mit
+pipeline_tag: text-generation
+---
+# 🚀 MoQ: Mixture of Quants
+> **MoQ (Mixture of Quants)** is a surgical evolution in model compression. By deploying an **Empirical Per-Tensor Analysis**, MoQ identifies "High-Intelligence" tensors and shields them with high-bit precision, while crushing redundant weights into extreme efficiency.
+---
+## 📊 MoQ vs. Unsloth: Performance Comparison
+The following benchmarks compare **MoQ 4.84** against **Unsloth Dynamic Quants**.
+*Note: Lower KLD (Kullback–Leibler Divergence) indicates higher fidelity to the original model.*
+### 📉 Key Divergence Metrics (Lower is Better)
+#### **Mean KLD**
+Average divergence across all layers. MoQ maintains a significantly lower average error profile.
+![Mean_KLD](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/vongwACIdL5vim_VLwcIn.png)
+#### **Maximum KLD**
+The "worst-case" divergence point. MoQ 4.84 effectively eliminates the extreme divergence spikes seen in standard dynamic quants.
+![Maximum_KLD](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/ThF0qh5xS45y1iwVeWuH7.png)
+#### **RMS Δp**
+Root Mean Square change in probabilities. This measures the stability of the model's confidence.
+![RMS_Δp](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/0pglHo80A6IjJRjPM0Zjs.png)
+---
+### 📈 Precision Percentiles
+These graphs demonstrate MoQ's ability to maintain stability even within the most sensitive portions of the architecture.
+#### **95.0% Percentile KLD**
+![95.0pct_KLD](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/G9jJMxUgBUR0unkU3fX6q.png)
+#### **99.0% Percentile KLD**
+![99.0pct_KLD](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/HhgKNfcXXyMio5z84vIGI.png)
+#### **99.9% Percentile KLD**
+![99.9pct_KLD](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/ZqlPEtkIlCRH_0HL3-nlm.png)
+---
+### 🎯 Token Match (Same Top-P)
+This metric tracks how often the quantized model chooses the **exact same top token** as the original high-precision model.
+![Same_top_p](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/uSciX-oKfWFTwkPxOvvPO.png)
+---
+## 🧠 The MoQ Edge
+MoQ optimizes the architecture for the **Pareto frontier** of memory and performance.
+* **Dynamic Bitrate Allocation:** No more "one-size-fits-all." MoQ assigns precision where it actually matters.
+* **Cognitive Preservation:** Massive VRAM savings with near-zero degradation in logic and coherence.
+* **Next-Gen Efficiency:** Fits "Large" model intelligence into "Small" model hardware.
+### 📍 Weight Sensitivity Heatmap
+Lighter regions represent mission-critical tensors preserved at higher precision.
+![importance_heatmap](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/xrFfZoqhPquwX9EUl3D5O.png)
+### 📈 Importance Distribution
+The histogram shows the importance scores used to mathematically determine the optimal quant for each tensor.
+![importance_histogram](https://cdn-uploads.huggingface.co/production/uploads/69ac7f5db2b3b515d77e2278/FgJJohu80wXmZqIH4ifwB.png)
+---
+## 📥 Available Quants
+| Folder Link | BPW | Total Size | Description |
+| :--- | :---: | :---: | :--- |
+| [📂 **MoQ-2.55**](https://huggingface.co/WaleedAhmad/Qwen3.5-9B-GGUF-MoQ/blob/main/MoQ/Qwen3.5-9B-MoQ-2.55gguf) | **2.55** | **~2.88 GB** |
+---
+## 🛠 Usage & Deployment
+```bash
+./llama-cli -m Qwen3.5-9B-MoQ-4.84.gguf -p "The future of efficient AI is..."