Skip to main content

Note: langvision is now part of the unified langtrain SDK. pip install langtrain[vision] includes everything from langvision plus AdaptiveRank, DatasetIntelligence, and text LLM support. langvision continues to receive updates and remains fully supported.


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 Start • Features • Models • Docs


⚡ Quick Start

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

curl -fsSL https://raw.githubusercontent.com/langtrain-ai/langtrain_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

Discord • Twitter • Website


Built with ❤️ by Langtrain AI

Making vision AI accessible to everyone.

Metadata

Release files for langvision 0.1.65

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for langvision 0.1.65
File Size Uploaded Provenance Actions
langvision-0.1.65.tar.gz 147.7 kB Without provenance Details for langvision-0.1.65.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for langvision 0.1.65
File Interpreter ABI Platform Actions
langvision-0.1.65-py3-none-any.whl Python 3 none any Details for langvision-0.1.65-py3-none-any.whl

Total release size: 326.9 kB

Release files / langvision-0.1.65.tar.gz

Download URL langvision-0.1.65.tar.gz
Size 147.7 kB
Tags Source
SHA-256 checksum
How to use checksums
a46610596c02c8f4dffa7b2514af89e6b45d507aa4e8eccc8437e31fef67d625
BLAKE2b-256 checksum
How to use checksums
98598cc9ad166c53e84a4a2f34a940d14d9da62571e0966755b32c4ec970b229
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.17

Release files / langvision-0.1.65-py3-none-any.whl

Download URL langvision-0.1.65-py3-none-any.whl
Size 179.2 kB
Tags Python 3
SHA-256 checksum
How to use checksums
84fc52d712dfbdedecc70f4fede30473c5bee7bc2c4874b7a2237d39c68daf76
BLAKE2b-256 checksum
How to use checksums
f7f21eef7d1461ad26e3918920a0662ffc061e8b0926804386ac5be8f0eb3ef0
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.11.17

Security for langvision 0.1.65

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release

0.1.65 This release

2 release files No provenance

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page