Instructions to use Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jakelolipopp/Qwen3.5-9B-AltText-v4-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": "Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M

Ollama
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with Ollama:
```
ollama run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-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": "Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with Docker Model Runner:
```
docker model run hf.co/Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M
```

Lemonade

How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.5-9B-AltText-v4-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-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 Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Jakelolipopp/Qwen3.5-9B-AltText-v4-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 "Jakelolipopp/Qwen3.5-9B-AltText-v4-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-AltText-v4 - GGUF

This repository contains GGUF quantized models for Jakelolipopp/Qwen3.5-9B-AltText-v4-LORA, fine-tuned from base model unsloth/Qwen3.5-9B. Full merged safetensors weights are available at Jakelolipopp/Qwen3.5-9B-AltText-v4-merged.

The models and multimodal vision projector (mmproj) were converted and quantized using llama.cpp.

Available Quantization Files

File Name	Size
`Qwen3.5-9B-AltText-v4-BF16.gguf`	16.69 GB
`Qwen3.5-9B-AltText-v4-Q2_K.gguf`	3.56 GB
`Qwen3.5-9B-AltText-v4-Q3_K_M.gguf`	4.31 GB
`Qwen3.5-9B-AltText-v4-Q4_K_M.gguf`	5.24 GB
`Qwen3.5-9B-AltText-v4-Q5_K_M.gguf`	6.02 GB
`Qwen3.5-9B-AltText-v4-Q6_K.gguf`	6.85 GB
`Qwen3.5-9B-AltText-v4-Q8_0.gguf`	8.87 GB
`mmproj-Qwen3.5-9B-AltText-v4-BF16.gguf`	0.86 GB
`mmproj-Qwen3.5-9B-AltText-v4-Q8_0.gguf`	0.58 GB

Quantization Details

Base Models

Q8_0: Near-lossless quality, highest precision (recommended for maximum accuracy).
Q6_K: High quality, virtually indistinguishable from full precision with noticeable size reduction.
Q5_K_M: Great balance between quality and speed/VRAM usage (Recommended default).
Q4_K_M: Fast inference, compact size, great for lower memory devices.
Q3_K_M / Q2_K: Ultra-compressed for minimal resource footprints.

Multimodal Projectors (`mmproj`)

mmproj-...-BF16.gguf: Full bfloat16 vision projector for maximum visual fidelity.
mmproj-...-Q8_0.gguf: 8-bit quantized vision projector with lower VRAM overhead.

How to Run with llama.cpp

Make sure you pass both the model (-m) and the vision projector (--mmproj):

# Run interactive CLI with an image
./llama-cli \
  -m Qwen3.5-9B-AltText-v4-Q5_K_M.gguf \
  --mmproj mmproj-Qwen3.5-9B-AltText-v4-BF16.gguf \
  --image /path/to/image.jpg \
  -p "Generate detailed alternative text describing this image:" \
  -c 4096 --temp 0.2

Running Server Mode

./llama-server \
  -m Qwen3.5-9B-AltText-v4-Q5_K_M.gguf \
  --mmproj mmproj-Qwen3.5-9B-AltText-v4-BF16.gguf \
  --port 8080 -c 4096

How to Run with Ollama

Create a Modelfile:

FROM ./Qwen3.5-9B-AltText-v4-Q5_K_M.gguf
MMPROJ ./mmproj-Qwen3.5-9B-AltText-v4-BF16.gguf

PARAMETER temperature 0.2
PARAMETER top_p 0.95

Build and run the model:

ollama create qwen3.5-9b-alttext-v4 -f Modelfile
ollama run qwen3.5-9b-alttext-v4 "Describe this image: /path/to/image.png"

Downloads last month: 196

GGUF

Model size

9B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

unsloth/Qwen3.5-9B

Quantized

(30)

this model

Qwen3.5-9B-AltText-v4 - GGUF

Available Quantization Files

Quantization Details

Base Models

Multimodal Projectors (mmproj)

How to Run with llama.cpp

Running Server Mode

How to Run with Ollama

Model tree for Jakelolipopp/Qwen3.5-9B-AltText-v4-GGUF

Multimodal Projectors (`mmproj`)