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Llava

大型语言与视觉助手。支持视觉指令微调（instruction tuning）和基于图像的对话。将 CLIP 视觉编码器与 Vicuna/LLaMA 语言模型相结合。支持多轮图像对话、视觉问答（VQA）和指令跟随。适用于视觉语言聊天机器人或图像理解任务。最适合对话式图像分析。

Skill 元数据​

来源可选 — 通过 aigenlabs skills install official/mlops/llava 安装
路径optional-skills/mlops/llava
版本1.0.0
作者Orchestra Research
许可证MIT
依赖项transformers, torch, pillow
平台linux, macos, windows
标签LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA

参考：完整 SKILL.md​

信息

以下是 AigenLabs 在触发该 skill 时加载的完整 skill 定义。这是 skill 激活时 agent 所看到的指令内容。

LLaVA - 大型语言与视觉助手

用于对话式图像理解的开源视觉语言模型。

何时使用 LLaVA​

适用场景：

  • 构建视觉语言聊天机器人
  • 视觉问答（VQA）
  • 图像描述与字幕生成
  • 多轮图像对话
  • 视觉指令跟随
  • 含图像的文档理解

指标：

  • GitHub 23,000+ 星标
  • GPT-4V 级别能力（目标）
  • Apache 2.0 许可证
  • 多种模型规格（7B–34B 参数）

改用其他方案的情况：

  • GPT-4V：质量最高，基于 API
  • CLIP：简单零样本分类
  • BLIP-2：更适合纯字幕生成
  • Flamingo：研究用途，非开源

快速开始​

安装​

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

# Install
pip install -e .

基本用法​

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)

可用模型​

模型参数量显存质量
LLaVA-v1.5-7B7B~14 GB良好
LLaVA-v1.5-13B13B~28 GB较好
LLaVA-v1.6-34B34B~70 GB最佳
# 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 用法​

# 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）​

# 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

多轮对话​

# 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)

常见任务​

图像字幕生成​

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

视觉问答​

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

目标检测（文本形式）​

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

场景理解​

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

文档理解​

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

训练自定义模型​

# 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

量化（降低显存占用）​

# 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×

最佳实践​

  1. 从 7B 模型开始 — 质量良好，显存需求可控
  2. 使用 4-bit 量化 — 显著降低显存占用
  3. 需要 GPU — CPU 推理极慢
  4. 清晰的 prompt — 具体问题能获得更好的答案
  5. 多轮对话 — 保持对话上下文
  6. 温度 0.2–0.7 — 平衡创造性与一致性
  7. max_new_tokens 512–1024 — 用于详细回复
  8. 批量处理 — 按顺序处理多张图像

性能​

模型显存（FP16）显存（4-bit）速度（tokens/s）
7B~14 GB~4 GB~20
13B~28 GB~8 GB~12
34B~70 GB~18 GB~5

在 A100 GPU 上测试

基准测试​

LLaVA 在以下基准上取得了有竞争力的分数：

  • VQAv2：78.5%
  • GQA：62.0%
  • MM-Vet：35.4%
  • MMBench：64.3%

局限性​

  1. 幻觉 — 可能描述图像中不存在的内容
  2. 空间推理 — 难以精确定位位置
  3. 小字体文本 — 难以识别细小字体
  4. 目标计数 — 对大量目标计数不精确
  5. 显存需求 — 需要高性能 GPU
  6. 推理速度 — 比 CLIP 慢

与框架集成​

LangChain​

from langchain.llms.base import LLM

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

llm = LLaVALLM()

Gradio 应用​

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()

资源​