Instructions to use n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit")
config = load_config("n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit"

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 n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit

Run Hermes

hermes

OpenClaw new

How to use n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit"

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 "n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

llama-joycaption-beta-one-hf-llava-mlx-8Bit / README.md

n0kovo

Upload README.md with huggingface_hub

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base_model: fancyfeast/llama-joycaption-beta-one-hf-llava
tags:
  - captioning
  - mlx
  - mlx-my-repo
pipeline_tag: image-text-to-text
library_name: transformers

n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit

The Model n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit was converted to MLX format from fancyfeast/llama-joycaption-beta-one-hf-llava using mlx-lm version 0.28.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("n0kovo/llama-joycaption-beta-one-hf-llava-mlx-8Bit")

prompt="hello"

if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)