Instructions to use sh111111111111111/Qwen3.5-9B-BitClass-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sh111111111111111/Qwen3.5-9B-BitClass-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sh111111111111111/Qwen3.5-9B-BitClass-GGUF",
	filename="Qwen3.5-9B-MX-3.0bpw.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sh111111111111111/Qwen3.5-9B-BitClass-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

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

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

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass-GGUF

LM Studio
Jan

vLLM

How to use sh111111111111111/Qwen3.5-9B-BitClass-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-BitClass-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-BitClass-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass-GGUF

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

Unsloth Studio

How to use sh111111111111111/Qwen3.5-9B-BitClass-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 sh111111111111111/Qwen3.5-9B-BitClass-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 sh111111111111111/Qwen3.5-9B-BitClass-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sh111111111111111/Qwen3.5-9B-BitClass-GGUF to start chatting

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

Start the llama.cpp server

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

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": "sh111111111111111/Qwen3.5-9B-BitClass-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sh111111111111111/Qwen3.5-9B-BitClass-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 sh111111111111111/Qwen3.5-9B-BitClass-GGUF

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-BitClass-GGUF

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use sh111111111111111/Qwen3.5-9B-BitClass-GGUF with Docker Model Runner:
```
docker model run hf.co/sh111111111111111/Qwen3.5-9B-BitClass-GGUF
```

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3.5-9B-BitClass-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

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

Mixed-precision GGUF quantizations of Qwen3.5-9B using learned per-tensor quantization profiles. Each tensor group receives the precision level that minimizes quality loss for its importance — more bits where they matter, fewer where they don't.

Qwen3.5-9B uses a hybrid DeltaNet + Attention architecture (24 DeltaNet layers + 8 standard attention layers). Our pipeline includes full support for DeltaNet's unique tensor groups (in_proj_qkv, in_proj_z, out_proj) alongside standard attention and MLP tensors — 10 suffix groups, 250 weight tensors total.

Seven precision levels from compact (3.0 bpw) to high quality (5.0 bpw).

Models

File	Target BPW	Size	PPL ↓	Family
Qwen3.5-9B-MX-3.0bpw.gguf	3.0	3.79 GB	2.099	IQ
Qwen3.5-9B-MX-3.2bpw.gguf	3.2	3.82 GB	2.072	IQ
Qwen3.5-9B-MX-3.5bpw.gguf	3.5	4.38 GB	1.957	KQ
Qwen3.5-9B-MX-3.8bpw.gguf	3.8	4.48 GB	1.957	KQ
Qwen3.5-9B-MX-4.0bpw.gguf	4.0	4.59 GB	1.922	KQ
Qwen3.5-9B-MX-4.5bpw.gguf	4.5	5.24 GB	1.879	KQ
Qwen3.5-9B-MX-5.0bpw.gguf	5.0	5.46 GB	~1.87	KQ

Target BPW is the planner's per-tensor bit budget (and the filename label). The actual whole-file BPW runs ~0.3–0.4 higher, because output/embedding tensors are kept at higher precision and GGUF carries metadata overhead — see the Size column for the real footprint.

Recommended: MX-3.5bpw for the best quality-to-size ratio. MX-3.0bpw for maximum compression. MX-4.5bpw for the highest quality in this ladder.

How It Compares

Model	BPW	Size	PPL ↓	Source
ByteShape IQ3_S 2.81	2.81	3.15 GB	2.218	byteshape
ByteShape IQ3_S 3.00	3.00	3.37 GB	2.069	byteshape
★ Ours MX-3.0	3.0	3.79 GB	2.099	This repo
ByteShape IQ3_S 3.15	3.15	3.53 GB	2.033	byteshape
★ Ours MX-3.2	3.2	3.82 GB	2.072	This repo
★ Ours MX-3.5	3.5	4.38 GB	1.957	This repo
ByteShape IQ4_XS 3.60	3.60	4.04 GB	1.947	byteshape
★ Ours MX-4.0	4.0	4.59 GB	1.922	This repo
ByteShape IQ4_XS 4.20	4.20	4.71 GB	1.866	byteshape
Bartowski Q3_K_S	4.40	4.93 GB	1.898	bartowski
★ Ours MX-4.5	4.5	5.24 GB	1.879	This repo
★ Ours MX-5.0	5.0	5.46 GB	~1.87	This repo
Bartowski Q4_K_M	5.50	6.17 GB	1.856	bartowski

All models benchmarked in the same session on identical hardware (NVIDIA GB10 ATOM, GPU) for fair comparison.

Key Results

Near-parity with ByteShape at low BPW: MX-3.0 (PPL 2.099) vs ByteShape 3.00 (PPL 2.069) — 1.4% behind, at a larger file (3.79 vs 3.37 GB)
Competitive at mid-range: MX-3.5 (PPL 1.957) vs ByteShape 3.60 (PPL 1.947) — 0.5% behind, at 4.38 vs 4.04 GB
Beats Bartowski Q3_K_S: MX-4.5 (PPL 1.879) vs Bartowski Q3_K_S (PPL 1.898) — 1.0% better, at a larger file (5.24 vs 4.93 GB)

DeltaNet Architecture

Qwen3.5-9B is not a standard transformer. It uses a hybrid architecture:

24 DeltaNet layers with linear attention (in_proj_qkv, in_proj_z, out_proj tensors)
8 standard attention layers (q_proj, k_proj, v_proj, o_proj)
32 MLP layers (gate_proj, up_proj, down_proj)

Our pipeline handles all 10 tensor suffix groups with appropriate quantization profiles for each.

Running with llama.cpp

# Chat
llama-cli -m Qwen3.5-9B-MX-3.5bpw.gguf -cnv

# Server (OpenAI-compatible API)
llama-server -m Qwen3.5-9B-MX-3.5bpw.gguf --port 8080

# Benchmark
llama-perplexity -m Qwen3.5-9B-MX-3.5bpw.gguf -f your_eval_data.txt

Benchmarking Details

All benchmarks run with llama.cpp (commit 406f4e3) on NVIDIA GB10 ATOM GPU with full offload (-ngl 999). Perplexity measured via llama-perplexity on a held-out evaluation set (20 chunks, 512 context). Throughput via llama-bench (512 prompt / 128 generation tokens). All models benchmarked in the same session.

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.

Acknowledgments

Base model by Qwen Team
Importance matrix data from Bartowski
Quantization infrastructure built on llama.cpp

Downloads last month: 2,594

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(295)

this model