Instructions to use sh111111111111111/Qwen3.5-9B-BitClass2-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 sh111111111111111/Qwen3.5-9B-BitClass2-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 sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

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 sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

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 sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sh111111111111111/Qwen3.5-9B-BitClass2-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": "sh111111111111111/Qwen3.5-9B-BitClass2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Ollama
How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with Ollama:
```
ollama run hf.co/sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with Docker Model Runner:
```
docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M
```

Lemonade

How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-BitClass2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sh111111111111111/Qwen3.5-9B-BitClass2-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 sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

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 sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sh111111111111111/Qwen3.5-9B-BitClass2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M

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 "sh111111111111111/Qwen3.5-9B-BitClass2-GGUF:Q4_K_M" \
  --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"

Qwen3.5-9B — BitClass2 Mixed-Precision GGUF

Mixed-precision GGUF quantizations of Qwen3.5-9B using Hessian-informed per-tensor bit allocation. Each tensor group receives the precision level that minimizes quality loss for its measured sensitivity.

Available Quantizations

File	BPW	Size	PPL ↓	tok/s	Use Case
`Qwen3.5-9B-Q8_0.gguf`	8.5	9.53 GB	1.728	6.5	Near-lossless reference
`Qwen3.5-9B-Q6_K.gguf`	5.6	6.30 GB	1.863	8.3	High quality
`Qwen3.5-9B-Q5_K_M.gguf`	5.1	5.70 GB	1.865	8.8	Balanced quality and size
`Qwen3.5-9B-Q4_K_M.gguf`	4.7	5.21 GB	1.875	9.2	Best quality-to-size ratio
`Qwen3.5-9B-Q3_K_S.gguf`	3.3	3.66 GB	2.001	11.5	Maximum compression

Recommended: Q4_K_M — nearly matches Q6_K quality (PPL 1.875 vs 1.863) at 17% less size.

How It Compares

Model	BPW	Size	PPL ↓	Source
ByteShape IQ3_S 3.00bpw	3.0	3.37 GB	2.069	byteshape
★ Ours Q3_K_S	3.3	3.66 GB	2.001	This repo
★ Ours Q4_K_M	4.7	5.21 GB	1.875	This repo
★ Ours Q5_K_M	5.1	5.70 GB	1.865	This repo

Our Q3_K_S beats ByteShape's 3.00bpw 9B on perplexity (2.001 vs 2.069 — 3.3% better), at a larger file (3.66 vs 3.37 GB). ByteShape's higher-BPW rows reach lower PPL.

The PPL curve is remarkably flat from Q6_K to Q4_K_M: going from 6.30 GB down to 5.21 GB (saving ~1.1 GB) only costs 0.012 PPL. This is the mixed-precision allocation working — gate_proj/up_proj drop to Q4_K while down_proj and attention stay at Q6_K.

Key Sensitivity Findings (Qwen3.5-9B)

The Hessian sensitivity pattern for 9B is fundamentally different from 4B:

blk.3 (early layer) is most sensitive — score 1.0 for k/v. On 4B it was blk.34 (late layer).
Sensitivity peaks at both ends AND middle: blk.3 (1.0), blk.7 (0.78), blk.23 (0.78), blk.27 (0.86), blk.31 (0.87)
ffn_down at blk.4-5 is near-zero sensitivity (0.0003) — safe for aggressive quantization
This confirms: model-specific Hessian data matters. You cannot assume late layers are always most sensitive.

How It Works

Hessian sensitivity — compute H_diag = mean(X²) per layer on calibration data
LP-optimal allocation — solve knapsack: minimize Σ(sensitivity × quant_error) subject to size ≤ target
Per-layer variation — within each suffix group, vary types by layer using imatrix + Hessian blend
GGUF export — llama.cpp --tensor-type-file for per-tensor overrides

Usage

huggingface-cli download sh111111111111111/Qwen3.5-9B-BitClass2-GGUF \
    Qwen3.5-9B-Q4_K_M.gguf --local-dir .

llama-cli -m Qwen3.5-9B-Q4_K_M.gguf -cnv
llama-server -m Qwen3.5-9B-Q4_K_M.gguf --port 8080

Benchmark Details

NVIDIA GB10 ATOM (128GB unified memory, aarch64). llama.cpp commit 406f4e3. PPL via llama-perplexity (2 chunks, 851 context). tok/s via llama-bench (tg128, ngl=999).

Disclaimer

Independent project. Not affiliated with or endorsed by Qwen, Unsloth, ByteShape, Bartowski, or llama.cpp. Competitor figures are from our own benchmark harness and may differ from those projects' self-reported numbers; competitor file sizes reflect the revision we tested and may since have changed.

License

Apache 2.0, inherited from Qwen3.5-9B.

Downloads last month: 227

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for sh111111111111111/Qwen3.5-9B-BitClass2-GGUF

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