Instructions to use jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use jc-builds/Qwen3.5-9B-Q4_K_M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jc-builds/Qwen3.5-9B-Q4_K_M-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": "jc-builds/Qwen3.5-9B-Q4_K_M-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Ollama
How to use jc-builds/Qwen3.5-9B-Q4_K_M-GGUF with Ollama:
```
ollama run hf.co/jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M
```

Unsloth Studio

How to use jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jc-builds/Qwen3.5-9B-Q4_K_M-GGUF to start chatting

How to use jc-builds/Qwen3.5-9B-Q4_K_M-GGUF with Pi:

Start the llama.cpp server

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

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": "jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use jc-builds/Qwen3.5-9B-Q4_K_M-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf jc-builds/Qwen3.5-9B-Q4_K_M-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 "jc-builds/Qwen3.5-9B-Q4_K_M-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"

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

Lemonade

How to use jc-builds/Qwen3.5-9B-Q4_K_M-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-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 jc-builds/Qwen3.5-9B-Q4_K_M-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwen3.5-9B Q4_K_M GGUF

4-bit quantized GGUF of Qwen/Qwen3.5-9B optimized for on-device iOS inference via llama.cpp. The most capable model you can run on an iPhone.

Property	Value
Parameters	9 billion
Quantization	Q4_K_M (4-bit, medium quality)
File Size	5.3 GB
Context Window	262,144 tokens (native)
Architecture	Hybrid Gated DeltaNet + Attention
License	Apache 2.0
Languages	201 languages/dialects

Running It

This model is available directly in the HaploAI iOS app (v1.18+). Download it from the model selection page.

Key Features

Best-in-Class On-Device AI: Matches or beats models 9-13x its size
Thinking Mode: <think>...</think> chain-of-thought reasoning
Hybrid Architecture: Gated DeltaNet + Attention for efficient, high-quality inference
Natively Multimodal: Trained with early vision fusion
Massive Context: 262K native, extendable to 1M+ with YaRN

Benchmarks

Qwen3.5-9B vs Models 9-13x Larger

The 9B model beats Qwen3-80B on GPQA Diamond (81.7 vs 77.2), IFEval (91.5 vs 88.9), and HMMT math (83.2 vs 73.7). It also outperforms GPT-OSS-120B on MMLU-Pro (82.5 vs 80.8) and GPQA Diamond (81.7 vs 80.1).

MMLU-Pro: Size Class Comparison

Model	Params	MMLU-Pro
Qwen3.5-9B	9B	82.5
Qwen3-30B	30B	80.9
GPT-OSS-120B	120B	80.8
Qwen3.5-4B	4B	79.1
Gemma2-9B	9B	~55*
Phi-4-mini	3.8B	52.8

On-Device Inference Speed

Vision Capabilities

The 9B model outperforms the dedicated Qwen3-VL-30B (3x its size) on MMMU, MMMU-Pro, MathVision, OmniDocBench, and VideoMME.

Full Benchmark Table

Benchmark	Qwen3.5-9B	Qwen3-30B	Qwen3-80B	GPT-OSS-120B
MMLU-Pro	82.5	80.9	82.7	80.8
MMLU-Redux	91.1	91.4	92.5	91.0
GPQA Diamond	81.7	73.4	77.2	80.1
IFEval	91.5	88.9	88.9	-
HMMT Feb 25	83.2	63.1	73.7	76.7
HMMT Nov 25	82.9	73.8	81.2	81.8
LiveCodeBench v6	65.6	66.0	68.7	82.7
BFCL-V4 (Tool Use)	66.1	42.4	-	-
C-Eval	88.2	87.4	89.7	76.2
SuperGPQA	58.2	56.8	60.8	54.6

Vision Benchmarks

Benchmark	Qwen3.5-9B	Qwen3-VL-30B	GPT-5-Nano
MMMU	78.4	76.0	75.8
MMMU-Pro	70.1	63.0	57.2
MathVision	78.9	65.7	62.2
OmniDocBench	87.7	86.8	55.9
VideoMME	84.5	79.9	71.7
OSWorld	41.8	30.6	-

Device Compatibility

Device	RAM	Compatible	Speed
iPhone 16 Pro Max	8 GB	Yes	~22-28 tok/s
iPhone 16 Pro	8 GB	Yes	~20-25 tok/s
iPhone 16 / 15 Pro	8 GB	Possible (tight)	~15-20 tok/s
iPhone 15 and older	6 GB	Not recommended	-
iPad Pro (M-series)	8-16 GB	Yes	~25-40 tok/s
Mac (Apple Silicon)	16+ GB	Yes	~30-50 tok/s

Note: The 9B model at 5.3 GB requires devices with 8 GB+ RAM. For older devices, use the Qwen3.5-4B instead.

Usage

With llama.cpp

# Download
huggingface-cli download jc-builds/Qwen3.5-9B-Q4_K_M-GGUF Qwen3.5-9B-Q4_K_M.gguf

# Run (with thinking mode)
./llama-cli -m Qwen3.5-9B-Q4_K_M.gguf -p "Prove that there are infinitely many primes." -ngl 99

With Ollama

ollama run qwen3.5:9b

Prompt Format

Uses ChatML format:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant

Thinking Mode

<|im_start|>assistant
<think>
Let me reason through this carefully...
First, assume there are finitely many primes p1, p2, ..., pn.
Consider N = p1 * p2 * ... * pn + 1.
N is not divisible by any pi, so either N is prime or has a prime factor not in our list.
This contradicts our assumption.
</think>
There are infinitely many primes. Here is Euclid's classic proof...

Architecture Details

Qwen3.5 introduces a hybrid Gated DeltaNet + Gated Attention architecture:

3:1 ratio: 3 layers of Gated DeltaNet (linear attention) per 1 layer of full softmax attention
32 total layers: 8 blocks x (3 DeltaNet + 1 Attention)
Hidden dimension: 4,096
Near-constant memory: DeltaNet layers maintain bounded memory
GQA: 16 query heads, 4 KV heads for attention layers
FFN intermediate: 12,288
RoPE: theta=10,000,000 with YaRN extension

Why Qwen3.5-9B?

This model represents a paradigm shift in on-device AI:

9B params that beat 80B: On GPQA Diamond, IFEval, and math benchmarks
Hybrid attention is the future: DeltaNet layers provide near-constant memory, enabling huge context on mobile
Natively multimodal: No separate vision encoder needed for basic image understanding
201 languages: Broadest language support in its class
Apache 2.0: Fully open, commercially usable

Credits

Original model: Qwen/Qwen3.5-9B by Alibaba Cloud
GGUF conversion: Unsloth
Quantization: Q4_K_M via llama.cpp
Optimized for iOS: jc-builds

Downloads last month: 3,254

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

4-bit

Model tree for jc-builds/Qwen3.5-9B-Q4_K_M-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(438)

this model