Instructions to use X-D-Lab/KarmaVLM-Qwen1_5-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use X-D-Lab/KarmaVLM-Qwen1_5-7B with Transformers:

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

pipe = pipeline("text-generation", model="X-D-Lab/KarmaVLM-Qwen1_5-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("X-D-Lab/KarmaVLM-Qwen1_5-7B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use X-D-Lab/KarmaVLM-Qwen1_5-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "X-D-Lab/KarmaVLM-Qwen1_5-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "X-D-Lab/KarmaVLM-Qwen1_5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/X-D-Lab/KarmaVLM-Qwen1_5-7B

SGLang

How to use X-D-Lab/KarmaVLM-Qwen1_5-7B 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 "X-D-Lab/KarmaVLM-Qwen1_5-7B" \
    --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": "X-D-Lab/KarmaVLM-Qwen1_5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "X-D-Lab/KarmaVLM-Qwen1_5-7B" \
        --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": "X-D-Lab/KarmaVLM-Qwen1_5-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use X-D-Lab/KarmaVLM-Qwen1_5-7B with Docker Model Runner:
```
docker model run hf.co/X-D-Lab/KarmaVLM-Qwen1_5-7B
```

🧘🏻‍♂️ KarmaVLM (相生)

👏 Introduction

KarmaVLM is a family of high efficiency and powerful visual language model (VLM) pretrained with interleaved image-text data at scale, enabling content comprehension, recognition, and multi-round conversations about images.

🎉 News

[2024/02] KarmaVLM is released.

⚡️Features

KarmaVLM offers the following features:

High Efficiency: KarmaVLM focuses on exploring the capabilities of small parametric quantitative models on multimodal tasks. So, KarmaVLM can be efficiently deployed on most GPU cards and personal computers, and even on end devices such as mobile phones.
Multi-round text-image conversations: KarmaVLM can take both text and images as inputs and produce text outputs. Currently, it supports multi-round visual question answering with one image.
Strong image comprehension: KarmaVLM is adept at analyzing visuals, making it an efficient tool for tasks like extracting, organizing, and summarizing information from images.

👨‍💻 Quick Start

Requirements and Installation

git clone https://github.com/X-D-Lab/KarmaVLM.git
cd KarmaVLM

conda create -n karmavlm python=3.10 -y
conda activate karmavlm

pip install --upgrade pip  # enable PEP 660 support
pip install -e .
pip install -e ".[train]"
pip install flash-attn --no-build-isolation

🌏 Demo

CLI Inference

python -m llava.serve.cli \
    --model-path /path/to/karmavlm/model \
    --image-file /path/to/the/test/image

Gradio Web UI

Starting the Controller

python -m llava.serve.gradio_web_server \
--controller http://localhost:10000 \
--model-list-mode reload
--share ##(optional)

Launching the Gradio Web Server

python -m llava.serve.model_worker \
--host 0.0.0.0 \
--controller http://localhost:10000 \
--port 40000 \
--worker http://localhost:40000 \
--model-path /path/to/karmavlm/model \

📋 License

This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses. The content of this project itself is licensed under the Apache license 2.0.

🙇‍ Architecture

We build our project based on LLaVA: Large Language and Vision Assistant.

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