Instructions to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="filvyb/llama-joycaption-beta-one-hf-llava-GGUF")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("filvyb/llama-joycaption-beta-one-hf-llava-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use filvyb/llama-joycaption-beta-one-hf-llava-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 filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

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 filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

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 filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

Use Docker

docker model run hf.co/filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "filvyb/llama-joycaption-beta-one-hf-llava-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": "filvyb/llama-joycaption-beta-one-hf-llava-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

SGLang

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "filvyb/llama-joycaption-beta-one-hf-llava-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "filvyb/llama-joycaption-beta-one-hf-llava-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "filvyb/llama-joycaption-beta-one-hf-llava-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "filvyb/llama-joycaption-beta-one-hf-llava-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with Ollama:
```
ollama run hf.co/filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
```
Unsloth Desktop

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

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": "filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with Docker Model Runner:
```
docker model run hf.co/filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K
```

Lemonade

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

Run and chat with the model

lemonade run user.llama-joycaption-beta-one-hf-llava-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use filvyb/llama-joycaption-beta-one-hf-llava-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 filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

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 filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use filvyb/llama-joycaption-beta-one-hf-llava-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K

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 "filvyb/llama-joycaption-beta-one-hf-llava-GGUF:Q6_K" \
  --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"

Model Card for Llama JoyCaption Beta One

Github

JoyCaption is an image captioning Visual Language Model (VLM) built from the ground up as a free, open, and uncensored model for the community to use in training Diffusion models.

Key Features:

Free and Open: Always released for free, open weights, no restrictions, and just like bigASP, will come with training scripts and lots of juicy details on how it gets built.
Uncensored: Equal coverage of SFW and NSFW concepts. No "cylindrical shaped object with a white substance coming out on it" here.
Diversity: All are welcome here. Do you like digital art? Photoreal? Anime? Furry? JoyCaption is for everyone. Pains are being taken to ensure broad coverage of image styles, content, ethnicity, gender, orientation, etc.
Minimal Filtering: JoyCaption is trained on large swathes of images so that it can understand almost all aspects of our world. almost. Illegal content will never be tolerated in JoyCaption's training.

Downloads last month: 33

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

6-bit

8-bit

Model tree for filvyb/llama-joycaption-beta-one-hf-llava-GGUF

Base model

google/siglip2-so400m-patch14-384

Finetuned

fancyfeast/llama-joycaption-beta-one-hf-llava

Quantized

(14)

this model