diff --git a/.gitattributes b/.gitattributes index 53d7257..21b3632 100644 --- a/.gitattributes +++ b/.gitattributes @@ -44,4 +44,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text -*tfevents* filter=lfs diff=lfs merge=lfs -text \ No newline at end of file +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md index 4606d05..a4c6ab2 100644 --- a/README.md +++ b/README.md @@ -1,47 +1,721 @@ --- -license: Apache License 2.0 - -#model-type: -##如 gpt、phi、llama、chatglm、baichuan 等 -#- gpt - -#domain: -##如 nlp、cv、audio、multi-modal -#- nlp - -#language: -##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa -#- cn - -#metrics: -##如 CIDEr、Blue、ROUGE 等 -#- CIDEr - -#tags: -##各种自定义,包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他 -#- pretrained - -#tools: -##如 vllm、fastchat、llamacpp、AdaSeq 等 -#- vllm +license: apache-2.0 +license_name: qwen +license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE +pipeline_tag: image-text-to-text +library_name: transformers +base_model: +- OpenGVLab/InternVL3-2B +base_model_relation: finetune +datasets: +- OpenGVLab/MMPR-v1.2 +language: +- multilingual +tags: +- internvl +- unsloth +- custom_code --- -### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重,可浏览“模型文件”页面获取。 -#### 您可以通过如下git clone命令,或者ModelScope SDK来下载模型 +
+ Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. +
+ +
+如果您是本模型的贡献者,我们邀请您根据模型贡献文档,及时完善模型卡片内容。
\ No newline at end of file +#### BNB 8-bit Quantization + +```python +import torch +from transformers import AutoTokenizer, AutoModel +path = "OpenGVLab/InternVL3-2B" +model = AutoModel.from_pretrained( + path, + torch_dtype=torch.bfloat16, + load_in_8bit=True, + low_cpu_mem_usage=True, + use_flash_attn=True, + trust_remote_code=True).eval() +``` + +#### Multiple GPUs + +The reason for writing the code this way is to avoid errors that occur during multi-GPU inference due to tensors not being on the same device. By ensuring that the first and last layers of the large language model (LLM) are on the same device, we prevent such errors. + +```python +import math +import torch +from transformers import AutoTokenizer, AutoModel + +def split_model(model_name): + device_map = {} + world_size = torch.cuda.device_count() + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + num_layers = config.llm_config.num_hidden_layers + # Since the first GPU will be used for ViT, treat it as half a GPU. + num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) + num_layers_per_gpu = [num_layers_per_gpu] * world_size + num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) + layer_cnt = 0 + for i, num_layer in enumerate(num_layers_per_gpu): + for j in range(num_layer): + device_map[f'language_model.model.layers.{layer_cnt}'] = i + layer_cnt += 1 + device_map['vision_model'] = 0 + device_map['mlp1'] = 0 + device_map['language_model.model.tok_embeddings'] = 0 + device_map['language_model.model.embed_tokens'] = 0 + device_map['language_model.output'] = 0 + device_map['language_model.model.norm'] = 0 + device_map['language_model.model.rotary_emb'] = 0 + device_map['language_model.lm_head'] = 0 + device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 + + return device_map + +path = "OpenGVLab/InternVL3-2B" +device_map = split_model('InternVL3-2B') +model = AutoModel.from_pretrained( + path, + torch_dtype=torch.bfloat16, + low_cpu_mem_usage=True, + use_flash_attn=True, + trust_remote_code=True, + device_map=device_map).eval() +``` + +### Inference with Transformers + +```python +import math +import numpy as np +import torch +import torchvision.transforms as T +from decord import VideoReader, cpu +from PIL import Image +from torchvision.transforms.functional import InterpolationMode +from transformers import AutoModel, AutoTokenizer + +IMAGENET_MEAN = (0.485, 0.456, 0.406) +IMAGENET_STD = (0.229, 0.224, 0.225) + +def build_transform(input_size): + MEAN, STD = IMAGENET_MEAN, IMAGENET_STD + transform = T.Compose([ + T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img), + T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC), + T.ToTensor(), + T.Normalize(mean=MEAN, std=STD) + ]) + return transform + +def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size): + best_ratio_diff = float('inf') + best_ratio = (1, 1) + area = width * height + for ratio in target_ratios: + target_aspect_ratio = ratio[0] / ratio[1] + ratio_diff = abs(aspect_ratio - target_aspect_ratio) + if ratio_diff < best_ratio_diff: + best_ratio_diff = ratio_diff + best_ratio = ratio + elif ratio_diff == best_ratio_diff: + if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]: + best_ratio = ratio + return best_ratio + +def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False): + orig_width, orig_height = image.size + aspect_ratio = orig_width / orig_height + + # calculate the existing image aspect ratio + target_ratios = set( + (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if + i * j <= max_num and i * j >= min_num) + target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1]) + + # find the closest aspect ratio to the target + target_aspect_ratio = find_closest_aspect_ratio( + aspect_ratio, target_ratios, orig_width, orig_height, image_size) + + # calculate the target width and height + target_width = image_size * target_aspect_ratio[0] + target_height = image_size * target_aspect_ratio[1] + blocks = target_aspect_ratio[0] * target_aspect_ratio[1] + + # resize the image + resized_img = image.resize((target_width, target_height)) + processed_images = [] + for i in range(blocks): + box = ( + (i % (target_width // image_size)) * image_size, + (i // (target_width // image_size)) * image_size, + ((i % (target_width // image_size)) + 1) * image_size, + ((i // (target_width // image_size)) + 1) * image_size + ) + # split the image + split_img = resized_img.crop(box) + processed_images.append(split_img) + assert len(processed_images) == blocks + if use_thumbnail and len(processed_images) != 1: + thumbnail_img = image.resize((image_size, image_size)) + processed_images.append(thumbnail_img) + return processed_images + +def load_image(image_file, input_size=448, max_num=12): + image = Image.open(image_file).convert('RGB') + transform = build_transform(input_size=input_size) + images = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, max_num=max_num) + pixel_values = [transform(image) for image in images] + pixel_values = torch.stack(pixel_values) + return pixel_values + +def split_model(model_name): + device_map = {} + world_size = torch.cuda.device_count() + config = AutoConfig.from_pretrained(model_path, trust_remote_code=True) + num_layers = config.llm_config.num_hidden_layers + # Since the first GPU will be used for ViT, treat it as half a GPU. + num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5)) + num_layers_per_gpu = [num_layers_per_gpu] * world_size + num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5) + layer_cnt = 0 + for i, num_layer in enumerate(num_layers_per_gpu): + for j in range(num_layer): + device_map[f'language_model.model.layers.{layer_cnt}'] = i + layer_cnt += 1 + device_map['vision_model'] = 0 + device_map['mlp1'] = 0 + device_map['language_model.model.tok_embeddings'] = 0 + device_map['language_model.model.embed_tokens'] = 0 + device_map['language_model.output'] = 0 + device_map['language_model.model.norm'] = 0 + device_map['language_model.model.rotary_emb'] = 0 + device_map['language_model.lm_head'] = 0 + device_map[f'language_model.model.layers.{num_layers - 1}'] = 0 + + return device_map + +# If you set `load_in_8bit=True`, you will need two 80GB GPUs. +# If you set `load_in_8bit=False`, you will need at least three 80GB GPUs. +path = 'OpenGVLab/InternVL3-2B' +device_map = split_model('InternVL3-2B') +model = AutoModel.from_pretrained( + path, + torch_dtype=torch.bfloat16, + load_in_8bit=False, + low_cpu_mem_usage=True, + use_flash_attn=True, + trust_remote_code=True, + device_map=device_map).eval() +tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True, use_fast=False) + +# set the max number of tiles in `max_num` +pixel_values = load_image('./examples/image1.jpg', max_num=12).to(torch.bfloat16).cuda() +generation_config = dict(max_new_tokens=1024, do_sample=True) + +# pure-text conversation (纯文本对话) +question = 'Hello, who are you?' +response, history = model.chat(tokenizer, None, question, generation_config, history=None, return_history=True) +print(f'User: {question}\nAssistant: {response}') + +question = 'Can you tell me a story?' +response, history = model.chat(tokenizer, None, question, generation_config, history=history, return_history=True) +print(f'User: {question}\nAssistant: {response}') + +# single-image single-round conversation (单图单轮对话) +question = 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