Skip to main content

Efficient LoRA Fine-Tuning for Vision LLMs with advanced CLI and model zoo

Project description

Langvision

Fine-tune Vision LLMs with ease

Train LLaVA, Qwen-VL, and other vision models in minutes.
The simplest way to create custom multimodal AI.

Product Hunt

PyPI Downloads License

Quick StartFeaturesModelsDocs


⚡ Quick Start

1-Click Install (Recommended)

The fastest way to get started. Installs Langvision in an isolated environment.

curl -fsSL https://raw.githubusercontent.com/langtrain-ai/langvision/main/scripts/install.sh | bash

Or using pip

pip install langvision

Fine-tune a vision model in 3 lines:

from langvision import LoRATrainer

trainer = LoRATrainer(model_name="llava-hf/llava-1.5-7b-hf")
trainer.train_from_file("image_data.jsonl")

Your custom vision model is ready.


✨ Features

🖼️ Multimodal Training

Train on images + text together. Perfect for VQA, image captioning, and visual reasoning.

🎯 Smart Defaults

Optimized configurations for each model architecture. Just point and train.

💾 Efficient Memory

LoRA + 4-bit quantization = Train 13B vision models on a single 24GB GPU.

🔧 Battle-Tested

Production-ready code used by teams building real-world vision applications.

🌐 All Major Models

LLaVA, Qwen-VL, CogVLM, InternVL, and more. Full compatibility.

☁️ Deploy Anywhere

Export to GGUF, ONNX, or deploy directly to Langtrain Cloud.


🤖 Supported Models

Model Parameters Memory Required
LLaVA 1.5 7B, 13B 8GB, 16GB
Qwen-VL 7B 8GB
CogVLM 17B 24GB
InternVL 6B, 26B 8GB, 32GB
Phi-3 Vision 4.2B 6GB

📖 Full Example

from langvision import LoRATrainer
from langvision.config import TrainingConfig, LoRAConfig

# Configure training
config = TrainingConfig(
    num_epochs=3,
    batch_size=2,
    learning_rate=2e-4,
    lora=LoRAConfig(rank=16, alpha=32)
)

# Initialize trainer
trainer = LoRATrainer(
    model_name="llava-hf/llava-1.5-7b-hf",
    output_dir="./my-vision-model",
    config=config
)

# Train on image-text data
trainer.train_from_file("training_data.jsonl")

📝 Data Format

{"image": "path/to/image1.jpg", "conversations": [{"from": "human", "value": "What's in this image?"}, {"from": "assistant", "value": "A cat sitting on a couch."}]}

🤝 Community

DiscordTwitterWebsite


Built with ❤️ by Langtrain AI

Making vision AI accessible to everyone.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

langvision-0.1.55.tar.gz (122.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

langvision-0.1.55-py3-none-any.whl (153.2 kB view details)

Uploaded Python 3

File details

Details for the file langvision-0.1.55.tar.gz.

File metadata

  • Download URL: langvision-0.1.55.tar.gz
  • Upload date:
  • Size: 122.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for langvision-0.1.55.tar.gz
Algorithm Hash digest
SHA256 0cee0cccf77047891f4e309bbc887fd2007e1831912e20eb3cd4e6de82f81e8b
MD5 377b7c9ee6e4d40af4ee5ad037c832f2
BLAKE2b-256 c08b6abff8ac64566aa2b5b032a50c05332a56c6be0cdd72af24c1d0ae84ce94

See more details on using hashes here.

File details

Details for the file langvision-0.1.55-py3-none-any.whl.

File metadata

  • Download URL: langvision-0.1.55-py3-none-any.whl
  • Upload date:
  • Size: 153.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.11.14

File hashes

Hashes for langvision-0.1.55-py3-none-any.whl
Algorithm Hash digest
SHA256 1a708a12b5c6780b64f2544be8bbf8bfa5c66e71b60f4291ee1342cbecea5a84
MD5 100fe4c4c1c047b25f231e9c0c251bb2
BLAKE2b-256 9850ddfbafb504a2d164d6e95d4ba1ffc09da955b7282ecc76754f68ce2662cc

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page