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nikravan/glm-4vq

sourceHugging Faceupdated 2y agoView on Hugging Face

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Model Card

MultiModal MultiLingual (3ML)

This model is 4bit quantized of glm-4v-9b Model (Less than 9G).

It excels in document, image, chart questioning answering and delivers superior performance over GPT-4-turbo-2024-04-09, Gemini 1.0 Pro, Qwen-VL-Max, and Claude 3 Opus.

Some part of the original Model changed and It can excute on free version of google colab.

Try it: ![Open In Colab](https://colab.research.google.com/drive/1aZGX9f5Yw1WbiOrS3TpvPkUJUPyYQU?usp=sharing)

[![Github Source]](https://github.com/nikravan1/3ML)

Note: For optimal performance with document and image understanding, please use English or Chinese. The model can still handle chat in any supported language.

About GLM-4V-9B

GLM-4V-9B is a multimodal language model with visual understanding capabilities. The evaluation results of its related classic tasks are as follows:

	MMBench-EN-Test	MMBench-CN-Test	SEEDBench_IMG	MMStar	MMMU	MME	HallusionBench	AI2D	OCRBench
	英文综合	中文综合	综合能力	综合能力	学科综合	感知推理	幻觉性	图表理解	文字识别
GPT-4o, 20240513	83.4	82.1	77.1	63.9	69.2	2310.3	55	84.6	736
GPT-4v, 20240409	81	80.2	73	56	61.7	2070.2	43.9	78.6	656
GPT-4v, 20231106	77	74.4	72.3	49.7	53.8	1771.5	46.5	75.9	516
InternVL-Chat-V1.5	82.3	80.7	75.2	57.1	46.8	2189.6	47.4	80.6	720
LlaVA-Next-Yi-34B	81.1	79	75.7	51.6	48.8	2050.2	34.8	78.9	574
Step-1V	80.7	79.9	70.3	50	49.9	2206.4	48.4	79.2	625
MiniCPM-Llama3-V2.5	77.6	73.8	72.3	51.8	45.8	2024.6	42.4	78.4	725
Qwen-VL-Max	77.6	75.7	72.7	49.5	52	2281.7	41.2	75.7	684
GeminiProVision	73.6	74.3	70.7	38.6	49	2148.9	45.7	72.9	680
Claude-3V Opus	63.3	59.2	64	45.7	54.9	1586.8	37.8	70.6	694
GLM-4v-9B	81.1	79.4	76.8	58.7	47.2	2163.8	46.6	81.1	786

This repository is the model repository of 4bit quantized of GLM-4V-9B model, supporting `8K` context length.

Quick Start

Use colab model or this python script.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from PIL import Image

device = "cuda"

modelPath="nikravan/glm-4vq"
tokenizer = AutoTokenizer.from_pretrained(modelPath, trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    modelPath,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
    device_map="auto"
)



query ='explain all the details in this picture'
image = Image.open("a3.png").convert('RGB')
#image=""
inputs = tokenizer.apply_chat_template([{"role": "user", "image": image, "content": query}],
                                       add_generation_prompt=True, tokenize=True, return_tensors="pt",
                                       return_dict=True)  # chat with image mode

inputs = inputs.to(device)

gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1}
with torch.no_grad():
    outputs = model.generate(**inputs, **gen_kwargs)
    outputs = outputs[:, inputs['input_ids'].shape[1]:]
    print(tokenizer.decode(outputs[0]))