llava

Original source / Raw SKILL.md

---
name: llava
description: Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA]
dependencies: [transformers, torch, pillow]
---

# LLaVA - Large Language and Vision Assistant

Open-source vision-language model for conversational image understanding.

## When to use LLaVA

**Use when:**
- Building vision-language chatbots
- Visual question answering (VQA)
- Image description and captioning
- Multi-turn image conversations
- Visual instruction following
- Document understanding with images

**Metrics**:
- **23,000+ GitHub stars**
- GPT-4V level capabilities (targeted)
- Apache 2.0 License
- Multiple model sizes (7B-34B params)

**Use alternatives instead**:
- **GPT-4V**: Highest quality, API-based
- **CLIP**: Simple zero-shot classification
- **BLIP-2**: Better for captioning only
- **Flamingo**: Research, not open-source

## Quick start

### Installation

```bash
# Clone repository
git clone https://github.com/haotian-liu/LLaVA
cd LLaVA

# Install
pip install -e .
```

### Basic usage

```python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from llava.conversation import conv_templates
from PIL import Image
import torch

# Load model
model_path = "liuhaotian/llava-v1.5-7b"
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path=model_path,
    model_base=None,
    model_name=get_model_name_from_path(model_path)
)

# Load image
image = Image.open("image.jpg")
image_tensor = process_images([image], image_processor, model.config)
image_tensor = image_tensor.to(model.device, dtype=torch.float16)

# Create conversation
conv = conv_templates["llava_v1"].copy()
conv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + "\nWhat is in this image?")
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()

# Generate response
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        input_ids,
        images=image_tensor,
        do_sample=True,
        temperature=0.2,
        max_new_tokens=512
    )

response = tokenizer.decode(output_ids[0], skip_special_tokens=True).strip()
print(response)
```

## Available models

| Model | Parameters | VRAM | Quality |
|-------|------------|------|---------|
| LLaVA-v1.5-7B | 7B | ~14 GB | Good |
| LLaVA-v1.5-13B | 13B | ~28 GB | Better |
| LLaVA-v1.6-34B | 34B | ~70 GB | Best |

```python
# Load different models
model_7b = "liuhaotian/llava-v1.5-7b"
model_13b = "liuhaotian/llava-v1.5-13b"
model_34b = "liuhaotian/llava-v1.6-34b"

# 4-bit quantization for lower VRAM
load_4bit = True  # Reduces VRAM by ~4×
```

## CLI usage

```bash
# Single image query
python -m llava.serve.cli \
    --model-path liuhaotian/llava-v1.5-7b \
    --image-file image.jpg \
    --query "What is in this image?"

# Multi-turn conversation
python -m llava.serve.cli \
    --model-path liuhaotian/llava-v1.5-7b \
    --image-file image.jpg
# Then type questions interactively
```

## Web UI (Gradio)

```bash
# Launch Gradio interface
python -m llava.serve.gradio_web_server \
    --model-path liuhaotian/llava-v1.5-7b \
    --load-4bit  # Optional: reduce VRAM

# Access at http://localhost:7860
```

## Multi-turn conversations

```python
# Initialize conversation
conv = conv_templates["llava_v1"].copy()

# Turn 1
conv.append_message(conv.roles[0], DEFAULT_IMAGE_TOKEN + "\nWhat is in this image?")
conv.append_message(conv.roles[1], None)
response1 = generate(conv, model, image)  # "A dog playing in a park"

# Turn 2
conv.messages[-1][1] = response1  # Add previous response
conv.append_message(conv.roles[0], "What breed is the dog?")
conv.append_message(conv.roles[1], None)
response2 = generate(conv, model, image)  # "Golden Retriever"

# Turn 3
conv.messages[-1][1] = response2
conv.append_message(conv.roles[0], "What time of day is it?")
conv.append_message(conv.roles[1], None)
response3 = generate(conv, model, image)
```

## Common tasks

### Image captioning

```python
question = "Describe this image in detail."
response = ask(model, image, question)
```

### Visual question answering

```python
question = "How many people are in the image?"
response = ask(model, image, question)
```

### Object detection (textual)

```python
question = "List all the objects you can see in this image."
response = ask(model, image, question)
```

### Scene understanding

```python
question = "What is happening in this scene?"
response = ask(model, image, question)
```

### Document understanding

```python
question = "What is the main topic of this document?"
response = ask(model, document_image, question)
```

## Training custom model

```bash
# Stage 1: Feature alignment (558K image-caption pairs)
bash scripts/v1_5/pretrain.sh

# Stage 2: Visual instruction tuning (150K instruction data)
bash scripts/v1_5/finetune.sh
```

## Quantization (reduce VRAM)

```python
# 4-bit quantization
tokenizer, model, image_processor, context_len = load_pretrained_model(
    model_path="liuhaotian/llava-v1.5-13b",
    model_base=None,
    model_name=get_model_name_from_path("liuhaotian/llava-v1.5-13b"),
    load_4bit=True  # Reduces VRAM ~4×
)

# 8-bit quantization
load_8bit=True  # Reduces VRAM ~2×
```

## Best practices

1. **Start with 7B model** - Good quality, manageable VRAM
2. **Use 4-bit quantization** - Reduces VRAM significantly
3. **GPU required** - CPU inference extremely slow
4. **Clear prompts** - Specific questions get better answers
5. **Multi-turn conversations** - Maintain conversation context
6. **Temperature 0.2-0.7** - Balance creativity/consistency
7. **max_new_tokens 512-1024** - For detailed responses
8. **Batch processing** - Process multiple images sequentially

## Performance

| Model | VRAM (FP16) | VRAM (4-bit) | Speed (tokens/s) |
|-------|-------------|--------------|------------------|
| 7B | ~14 GB | ~4 GB | ~20 |
| 13B | ~28 GB | ~8 GB | ~12 |
| 34B | ~70 GB | ~18 GB | ~5 |

*On A100 GPU*

## Benchmarks

LLaVA achieves competitive scores on:
- **VQAv2**: 78.5%
- **GQA**: 62.0%
- **MM-Vet**: 35.4%
- **MMBench**: 64.3%

## Limitations

1. **Hallucinations** - May describe things not in image
2. **Spatial reasoning** - Struggles with precise locations
3. **Small text** - Difficulty reading fine print
4. **Object counting** - Imprecise for many objects
5. **VRAM requirements** - Need powerful GPU
6. **Inference speed** - Slower than CLIP

## Integration with frameworks

### LangChain

```python
from langchain.llms.base import LLM

class LLaVALLM(LLM):
    def _call(self, prompt, stop=None):
        # Custom LLaVA inference
        return response

llm = LLaVALLM()
```

### Gradio App

```python
import gradio as gr

def chat(image, text, history):
    response = ask_llava(model, image, text)
    return response

demo = gr.ChatInterface(
    chat,
    additional_inputs=[gr.Image(type="pil")],
    title="LLaVA Chat"
)
demo.launch()
```

## Resources

- **GitHub**: https://github.com/haotian-liu/LLaVA ⭐ 23,000+
- **Paper**: https://arxiv.org/abs/2304.08485
- **Demo**: https://llava.hliu.cc
- **Models**: https://huggingface.co/liuhaotian
- **License**: Apache 2.0




---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### references/training.md

```markdown
# LLaVA Training Guide

Guide to training and fine-tuning LLaVA models.

## Training stages

### Stage 1: Feature alignment (Pretraining)

**Purpose**: Align vision encoder with language model

**Data**: 558K image-caption pairs (CC3M subset)

```bash
# Download pretrained projector or train from scratch
bash scripts/v1_5/pretrain.sh
```

**Configuration:**
- Base model: Vicuna-7B or LLaMA-2-7B
- Vision encoder: CLIP ViT-L/14
- Training time: ~20 hours on 8× A100

### Stage 2: Visual instruction tuning

**Purpose**: Teach model to follow visual instructions

**Data**: 150K GPT-generated multimodal instruction data

```bash
# Fine-tune with instruction data
bash scripts/v1_5/finetune.sh
```

**Configuration:**
- Epochs: 1
- Batch size: 128 (across 8 GPUs)
- Learning rate: 2e-5
- Training time: ~24 hours on 8× A100

## Data format

### Instruction data format

```json
[
    {
        "id": "001",
        "image": "path/to/image.jpg",
        "conversations": [
            {
                "from": "human",
                "value": "<image>\nWhat is in this image?"
            },
            {
                "from": "gpt",
                "value": "The image shows a dog playing in a park."
            },
            {
                "from": "human",
                "value": "What breed is the dog?"
            },
            {
                "from": "gpt",
                "value": "It appears to be a Golden Retriever."
            }
        ]
    }
]
```

## Fine-tuning on custom data

### Prepare your data

```python
import json

# Create instruction data
data = []
for image_path, qa_pairs in your_dataset:
    conversations = []
    for q, a in qa_pairs:
        conversations.append({"from": "human", "value": f"<image>\n{q}"})
        conversations.append({"from": "gpt", "value": a})

    data.append({
        "id": str(len(data)),
        "image": image_path,
        "conversations": conversations
    })

# Save
with open("custom_data.json", "w") as f:
    json.dump(data, f, indent=2)
```

### Fine-tune script

```bash
#!/bin/bash

# Set paths
DATA_PATH="custom_data.json"
IMAGE_FOLDER="path/to/images"
MODEL_PATH="liuhaotian/llava-v1.5-7b"
OUTPUT_DIR="./checkpoints/llava-custom"

# Fine-tune
deepspeed llava/train/train_mem.py \
    --deepspeed ./scripts/zero2.json \
    --model_name_or_path $MODEL_PATH \
    --version v1 \
    --data_path $DATA_PATH \
    --image_folder $IMAGE_FOLDER \
    --vision_tower openai/clip-vit-large-patch14-336 \
    --mm_projector_type mlp2x_gelu \
    --mm_vision_select_layer -2 \
    --mm_use_im_start_end False \
    --mm_use_im_patch_token False \
    --image_aspect_ratio pad \
    --group_by_modality_length True \
    --bf16 True \
    --output_dir $OUTPUT_DIR \
    --num_train_epochs 1 \
    --per_device_train_batch_size 16 \
    --per_device_eval_batch_size 4 \
    --gradient_accumulation_steps 1 \
    --evaluation_strategy "no" \
    --save_strategy "steps" \
    --save_steps 50000 \
    --save_total_limit 1 \
    --learning_rate 2e-5 \
    --weight_decay 0. \
    --warmup_ratio 0.03 \
    --lr_scheduler_type "cosine" \
    --logging_steps 1 \
    --tf32 True \
    --model_max_length 2048 \
    --gradient_checkpointing True \
    --dataloader_num_workers 4 \
    --lazy_preprocess True \
    --report_to wandb
```

## LoRA fine-tuning (memory efficient)

```python
from peft import LoraConfig, get_peft_model

# LoRA config
lora_config = LoraConfig(
    r=8,  # LoRA rank
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM"
)

# Apply LoRA
model = get_peft_model(base_model, lora_config)

# Train with much lower memory
```

## Hardware requirements

### Full fine-tuning

- **7B model**: 8× A100 (40GB)
- **13B model**: 8× A100 (80GB)
- **Training time**: 20-48 hours

### LoRA fine-tuning

- **7B model**: 1× A100 (40GB)
- **13B model**: 2× A100 (40GB)
- **Training time**: 10-24 hours

## Best practices

1. **Start with pretrained** - Don't train from scratch
2. **Use LoRA for efficiency** - 10× less memory
3. **Quality over quantity** - 1K high-quality > 10K low-quality
4. **Multi-turn conversations** - More engaging than single Q&A
5. **Diverse images** - Cover different scenarios
6. **Clear instructions** - Specific questions get better answers
7. **Monitor loss** - Should decrease smoothly
8. **Save checkpoints** - Training can fail
9. **Test regularly** - Validate on held-out set
10. **Use DeepSpeed** - For multi-GPU training

## Resources

- **Training script**: https://github.com/haotian-liu/LLaVA/tree/main/scripts
- **Data format**: https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md
- **Paper**: https://arxiv.org/abs/2304.08485

```