?https://github.com/OpenBMB/MiniCPM-V
?ingFang SC", "Hiragino Sans GB", "Microsoft YaHei UI", "Microsoft YaHei", Arial, sans-serif;font-size: var(--articleFontsize);letter-spacing: 0.034em;">https://huggingface.co/openbmb/MiniCPM-V-2_6
gitclonehttps://huggingface.co/openbmb/MiniCPM-V-2_6
gitclonehttps://github.com/vllm-project/vllm.gitcdvllmpipinstalle.
from PIL import Imagefrom transformers import AutoTokenizerfrom vllm import LLM, SamplingParams# 图像文件路径列表IMAGES = ["/root/ld/ld_project/MiniCPM-V/assets/airplane.jpeg",# 本地图片路径]# 模型名称或路径MODEL_NAME = "/root/ld/ld_model_pretrained/Minicpmv2_6"# 本地模型路径或Hugging Face模型名称# 打开并转换图像image = Image.open(IMAGES[0]).convert("RGB")# 初始化分词器tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)# 初始化语言模型llm = LLM(model=MODEL_NAME,gpu_memory_utilization=1,# 使用全部GPU内存trust_remote_code=True,max_model_len=2048)# 根据内存状况可调整此值# 构建对话消息messages = [{'role': 'user', 'content': '(<image>./</image>)\n' + '请描述这张图片'}]# 应用对话模板到消息prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)# 设置停止符ID# 2.0# stop_token_ids = [tokenizer.eos_id]# 2.5#stop_token_ids = [tokenizer.eos_id, tokenizer.eot_id]# 2.6stop_tokens = ['<|im_end|>', '<|endoftext|>']stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]# 设置生成参数sampling_params = SamplingParams(stop_token_ids=stop_token_ids,# temperature=0.7,# top_p=0.8,# top_k=100,# seed=3472,max_tokens=1024,# min_tokens=150,temperature=0,use_beam_search=True,# length_penalty=1.2,best_of=3)# 获取模型输出outputs = llm.generate({"prompt": prompt,"multi_modal_data": {"image": image}}, sampling_params=sampling_params)print(outputs[0].outputs[0].text)
from transformers import AutoTokenizerfrom decord import VideoReader, cpufrom PIL import Imagefrom vllm import LLM, SamplingParams# 进行图片推理MAX_NUM_FRAMES = 64def encode_video(filepath):def uniform_sample(l, n):gap = len(l) / nidxs = [int(i * gap + gap / 2) for i in range(n)]return [l[i] for i in idxs]vr = VideoReader(filepath, ctx=cpu(0))sample_fps = round(vr.get_avg_fps() / 1)# FPSframe_idx = [i for i in range(0, len(vr), sample_fps)]if len(frame_idx)>MAX_NUM_FRAMES:frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)video = vr.get_batch(frame_idx).asnumpy()video = [Image.fromarray(v.astype('uint8')) for v in video]return videoMODEL_NAME = "openbmb/MiniCPM-V-2_6" # or local model pathvideo = encode_video("xxx.mp4")messages = [{"role":"user","content":"".join(["(<image>./</image>)"] * len(video)) + \"\nPlease describe this video."}]prompt = tokenizer.apply_chat_template(messages,tokenize=False,add_generation_prompt=True)tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)llm = LLM(model=MODEL_NAME,gpu_memory_utilization=1,max_model_len=4096)stop_tokens = ['<|im_end|>', '<|endoftext|>']stop_token_ids = [tokenizer.convert_tokens_to_ids(i) for i in stop_tokens]sampling_params = SamplingParams(stop_token_ids=stop_token_ids,use_beam_search=Truetemperature=0,max_tokens=64)outputs = llm.generate({"prompt": prompt,"multi_modal_data": {"image": {"images": video,"use_image_id": False,"max_slice_nums": 1 if len(video) > 16 else 2}}}, sampling_params=sampling_params_beam)
gitclonehttps://github.com/vllm-project/vllm.gitcdvllmpipinstalle.
vllmserve/root/ld/ld_model_pretrained/Minicpmv2_6--dtypeauto--max-model-len2048--api-keytoken-abc123--gpu_memory_utilization1--trust-remote-code
from openai import OpenAIopenai_api_key = "token-abc123" # your api key set in launch serveropenai_api_base = "http://localhost:8000/v1" # http idclient = OpenAI(api_key=openai_api_key,base_url=openai_api_base,)chat_response = client.chat.completions.create(model="/root/ld/ld_model_pretrained/Minicpmv2_6", # model_local_path or huggingface idmessages=[{"role": "user","content": [# NOTE: 使用图像令牌 <image> 的提示格式是不必要的，因为提示将由API服务器自动处理。# 由于提示将由API服务器自动处理，因此不需要使用包含 <image> 图像令牌的提示格式。{"type": "text", "text": "请描述这张图片"},{"type": "image_url","image_url": {"url": "https://air-example-data-2.s3.us-west-2.amazonaws.com/vllm_opensource_llava/stop_sign.jpg",},},],}],extra_body={"stop_token_ids": [151645, 151643]})print("Chat response:", chat_response)print("Chat response content:", chat_response.choices[0].message.content)
3.2 传入本地图片
from openai import OpenAIopenai_api_key = "token-abc123" # your api key set in launch serveropenai_api_base = "http://localhost:8000/v1" # http idclient = OpenAI(api_key=openai_api_key,base_url=openai_api_base,)# 用于传本地图片with open('your/local/pic/path','rb') as file:image = "data:image/jpeg;base64,"+ base64.b64encode(file.read()).decode('utf-8')chat_response = client.chat.completions.create(model="/root/ld/ld_model_pretrained/Minicpmv2_6", # model_local_path or huggingface idmessages=[{"role": "user","content": [# NOTE: 使用图像令牌 <image> 的提示格式是不必要的，因为提示将由API服务器自动处理。# 由于提示将由API服务器自动处理，因此不需要使用包含 <image> 图像令牌的提示格式。{"type": "text", "text": "请描述这张图片"},{"type": "image_url","image_url": {"url": image,},},],}],extra_body={"stop_token_ids": [151645, 151643]})print("Chat response:", chat_response)print("Chat response content:", chat_response.choices[0].message.content)
设备要求：运行非量化版内存超过19g，运行量化版超过8g内存
brewinstallffmpegbrewinstallpkg-config
gitclone-bminicpm-v2.5https://github.com/OpenBMB/llama.cpp.git
cdllama.cppmake
a. 首先前往huggingface或者modelscope下载pytorch权重：
gitclonehttps://huggingface.co/openbmb/MiniCPM-V-2_6
#第一行为获得模型中间输出，为转换为gguf作准备python./examples/llava/minicpmv-convert/minicpmv2_6-surgery.py-m../MiniCPM-V-2_6#将siglip模型转换为ggufpython./examples/llava/minicpmv-convert/minicpmv2_6-convert-image-encoder-to-gguf.py-m../MiniCPM-V-2_6--minicpmv-projector../MiniCPM-V-2_6/minicpmv.projector--output-dir../MiniCPM-V-2_6/--image-mean0.50.50.5--image-std0.50.50.5#将语言模型转换为ggufpython./convert-hf-to-gguf.py../MiniCPM-V-2_6/model
#quantizeint4version./llama-quantize../MiniCPM-V-2_6/model/ggml-model-f16.gguf../MiniCPM-V-2_6/model/ggml-model-Q4_K_M.ggufQ4_K_M
方法二：
5. 开始推理：
5.1 图片推理指令
5.2 视频推理指令
./llama-minicpmv-cli-m/Users/liudan/Downloads/Minicpmv2_6gguf/ggml-model-Q4_K_M.gguf--mmproj/Users/liudan/Downloads/Minicpmv2_6gguf/mmproj-model-f16.gguf-c8192--temp0.7--top-p0.8--top-k100--repeat-penalty1.05--video./Minicpmv2_6gguf/test_vedieo.mp4-p"我接下来会给你一个视频，请告诉我视频中描述了什么"
gitclone-bminicpm-v2.6https://github.com/OpenBMB/ollama.gitcdollama/llm
brewinstallgocmakegcc
gogenerate./...
gobuild.
./ollamaserve
vimminicpmv2_6.Modelfile
FROM ./MiniCPM-V-2_6/model/ggml-model-Q4_K_M.ggufFROM ./MiniCPM-V-2_6/mmproj-model-f16.ggufTEMPLATE """{{ if .System }}<|im_start|>system{{ .System }}<|im_end|>{{ end }}{{ if .Prompt }}<|im_start|>user{{ .Prompt }}<|im_end|>{{ end }}<|im_start|>assistant<|im_end|>{{ .Response }}<|im_end|>"""PARAMETER stop "<|endoftext|>"PARAMETER stop "<|im_end|>"PARAMETER num_ctx 2048
ollamacreateminicpm2.6-fminicpmv2_6.Modelfile
ollamarunminicpm2.6
Whatisdescribedinthispicture?/Users/liudan/Desktop/11.jpg
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