ModelListadosExterno

VideoChat TPO

This model is based on the paper Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment.

Fontehuggingface.co/OpenGVLab/VideoChat-TPO/tree/7d227609f69ca03c1c3b8ace7a62ab39c07287ab

TipoOtherModelo basemistralai/Mistral-7B-Instruct-v0.2VersãoRevision 7d22760Bnota

Fonte do modelo

Tipo: Other
Tamanho do arquivo: 1,23–4,65 GB
Modelo base: mistralai/Mistral-7B-Instruct-v0.2
Versão: vRevision 7d22760
Atualizado: 1 de out. de 2026
Downloads: 27
Fonte: Hugging Face

Descrição da fonte

VideoChat2-TPO

This model is based on the paper Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment.

🏃 Installation

pip install -r requirements.txt
python app.py

🔧 Usage

from transformers import AutoModel, AutoTokenizer
from tokenizer import MultimodalLlamaTokenizer

model_path = "OpenGVLab/VideoChat-TPO"
tokenizer =  AutoTokenizer.from_pretrained(model_path,
trust_remote_code=True,
use_fast=False,)
model = AutoModel.from_pretrained(model_path,  trust_remote_code=True, _tokenizer=self.tokenizer).eval()

Fontes

1 fonte

Hugging FacePrimárioVersão 7d227609f69ca03c1c3b8ace7a62ab39c07287ab

huggingface.co/OpenGVLab/VideoChat-TPO/tree/7d227609f69ca03c1c3b8ace7a62ab39c07287ab

Verificado 22 de ago.

Artefatos de modelo

4 artefatos

model-00001-of-00004.safetensors

safetensors · 4,65 GB · SHA-256 c8640081ef34…a330 · Hugging Face

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model-00002-of-00004.safetensors

safetensors · 4,61 GB · SHA-256 8907fc1447bb…086c · Hugging Face

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model-00003-of-00004.safetensors

safetensors · 4,61 GB · SHA-256 5640bcb11fc2…4f93 · Hugging Face

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model-00004-of-00004.safetensors

safetensors · 1,23 GB · SHA-256 d0360c7932a2…4fe8 · Hugging Face

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Trechos de fonte

2 trechos

Readme22 de ago.huggingface.co/OpenGVLab/VideoChat-TPO

--- base_model: - mistralai/Mistral-7B-Instruct-v0.2 library_name: transformers license: mit pipeline_tag: video-text-to-text --- # VideoChat2-TPO This model is based on the paper [Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment](https://huggingface.co/papers/2412.19326). ## 🏃 Installation ``` pip install -r requirements.txt python app.py ``` ## 🔧 Usage ``` from transformers import AutoModel, AutoTokenizer from tokenizer import MultimodalLlamaTokenizer model_path = "OpenGVLab/VideoChat-TPO" tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True, use_fast=False,) model = AutoModel.from_pretrained(model_path, trust_remote_code=True, _tokenizer=self.tokenizer).eval() ```

Note22 de ago.huggingface.co/OpenGVLab/VideoChat-TPO

Source context: 27 downloads · 5 likes · Pipeline video-text-to-text · Library transformers · Repo OpenGVLab/VideoChat-TPO