Note:
langvisionis now part of the unifiedlangtrainSDK.pip install langtrain[vision]includes everything from langvision plus AdaptiveRank, DatasetIntelligence, and text LLM support. langvision continues to receive updates and remains fully supported.
Train LLaVA, Qwen-VL, and other vision models in minutes.
The simplest way to create custom multimodal AI.
Quick Start • Features • Models • Docs
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
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.
🖼️ Multimodal TrainingTrain on images + text together. Perfect for VQA, image captioning, and visual reasoning. 🎯 Smart DefaultsOptimized configurations for each model architecture. Just point and train. 💾 Efficient MemoryLoRA + 4-bit quantization = Train 13B vision models on a single 24GB GPU. |
🔧 Battle-TestedProduction-ready code used by teams building real-world vision applications. 🌐 All Major ModelsLLaVA, Qwen-VL, CogVLM, InternVL, and more. Full compatibility. ☁️ Deploy AnywhereExport to GGUF, ONNX, or deploy directly to Langtrain Cloud. |
| 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 |
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")
{"image": "path/to/image1.jpg", "conversations": [{"from": "human", "value": "What's in this image?"}, {"from": "assistant", "value": "A cat sitting on a couch."}]}
Built with ❤️ by Langtrain AI
Making vision AI accessible to everyone.
153 commits
59 commits
Python
98.7%
Note:
langvisionis now part of the unifiedlangtrainSDK.pip install langtrain[vision]includes everything from langvision plus AdaptiveRank, DatasetIntelligence, and text LLM support. langvision continues to receive updates and remains fully supported.
Train LLaVA, Qwen-VL, and other vision models in minutes.
The simplest way to create custom multimodal AI.
Quick Start • Features • Models • Docs
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
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.
🖼️ Multimodal TrainingTrain on images + text together. Perfect for VQA, image captioning, and visual reasoning. 🎯 Smart DefaultsOptimized configurations for each model architecture. Just point and train. 💾 Efficient MemoryLoRA + 4-bit quantization = Train 13B vision models on a single 24GB GPU. |
🔧 Battle-TestedProduction-ready code used by teams building real-world vision applications. 🌐 All Major ModelsLLaVA, Qwen-VL, CogVLM, InternVL, and more. Full compatibility. ☁️ Deploy AnywhereExport to GGUF, ONNX, or deploy directly to Langtrain Cloud. |
| 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 |
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")
{"image": "path/to/image1.jpg", "conversations": [{"from": "human", "value": "What's in this image?"}, {"from": "assistant", "value": "A cat sitting on a couch."}]}
Built with ❤️ by Langtrain AI
Making vision AI accessible to everyone.
153 commits
59 commits
Python
98.7%