InternLM-XComposer2

[💻Github Repo](https://github.com/InternLM/InternLM-XComposer) [Paper](https://arxiv.org/abs/2401.16420)

**InternLM-XComposer2** is a vision-language large model (VLLM) based on [InternLM2](https://github.com/InternLM/InternLM) for advanced text-image comprehension and composition. We release InternLM-XComposer2 series in two versions: - InternLM-XComposer2-VL: The pretrained VLLM model with InternLM2 as the initialization of the LLM, achieving strong performance on various multimodal benchmarks. - InternLM-XComposer2: The finetuned VLLM for *Free-from Interleaved Text-Image Composition*. This is the 4-bit version of InternLM-XComposer2, install the latest version of [auto_gptq](https://github.com/AutoGPTQ/AutoGPTQ#quick-installation) before using. ```python import torch, auto_gptq from PIL import Image from transformers import AutoModel, AutoTokenizer from auto_gptq.modeling import BaseGPTQForCausalLM auto_gptq.modeling._base.SUPPORTED_MODELS = ["internlm"] torch.set_grad_enabled(False) class InternLMXComposer2QForCausalLM(BaseGPTQForCausalLM): layers_block_name = "model.layers" outside_layer_modules = [ 'vit', 'vision_proj', 'model.tok_embeddings', 'model.norm', 'output', ] inside_layer_modules = [ ["attention.wqkv.linear"], ["attention.wo.linear"], ["feed_forward.w1.linear", "feed_forward.w3.linear"], ["feed_forward.w2.linear"], ] # init model and tokenizer model = InternLMXComposer2QForCausalLM.from_quantized( 'internlm/internlm-xcomposer2-7b-4bit', trust_remote_code=True, device="cuda:0").eval() tokenizer = AutoTokenizer.from_pretrained( 'internlm/internlm-xcomposer2-7b-4bit', trust_remote_code=True) img_path_list = [ 'panda.jpg', 'bamboo.jpeg', ] images = [] for img_path in img_path_list: image = Image.open(img_path).convert("RGB") image = model.vis_processor(image) images.append(image) image = torch.stack(images) query = ' please write an article based on the images. Title: my favorite animal.' with torch.cuda.amp.autocast(): response, history = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False) print(response) #My Favorite Animal: The Panda #The panda, also known as the giant panda, is one of the most beloved animals in the world. These adorable creatures are native to China and can be found in the wild in a few select locations, but they are more commonly seen in captivity at zoos or wildlife reserves. #Pandas have a distinct black-and-white coloration that makes them instantly recognizable. They are known for their love of bamboo, which they eat almost exclusively. In fact, pandas spend up to 14 hours a day eating, with the majority of their diet consisting of bamboo. Despite this seemingly unbalanced diet, pandas are actually quite healthy and have a low body fat percentage, thanks to their ability to digest bamboo efficiently. #In addition to their unique eating habits, pandas are also known for their playful personalities. They are intelligent and curious creatures, often engaging in activities like playing with toys or climbing trees. However, they do not typically exhibit these behaviors in the wild, where they are solitary creatures who prefer to spend their time alone. #One of the biggest threats to the panda's survival is habitat loss due to deforestation. As a result, many pandas now live in captivity, where they are cared for by dedicated staff and provided with enrichment opportunities to keep them engaged and stimulated. While it is important to protect these animals from extinction, it is also crucial to remember that they are still wild creatures and should be treated with respect and care. #Overall, the panda is an amazing animal that has captured the hearts of people around the world. Whether you see them in the wild or in captivity, there is no denying the charm and allure of these gentle giants. ``` ### Open Source License The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow free commercial usage. To apply for a commercial license, please fill in the application form (English)/申请表（中文）. For other questions or collaborations, please contact internlm@pjlab.org.cn.