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VideoChat2 HD Infinity Mistral 7B

HuggingFace-compatible model files for VideoChat2-Infinity (Mistral-7B), a long video understanding model that extends VideoChat2-HD with infinite-length video processing via continuous long-term attention.

TipoOtherModelo basemistralai/Mistral-7B-Instruct-v0.2VersãoRevision c4f85edBnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
1,40–4,66 GB
Modelo base
mistralai/Mistral-7B-Instruct-v0.2
Versão
vRevision c4f85ed
Atualizado
24 de out. de 2026
Downloads
18
Fonte
Hugging Face

Descrição da fonte

Infinite-VideoChat2

HuggingFace-compatible model files for VideoChat2-Infinity (Mistral-7B), a long video understanding model that extends VideoChat2-HD with infinite-length video processing via continuous long-term attention.

Model Architecture

  • Vision Encoder – UMT-L (ViT, 1024-dim, 24 layers)
  • Q-Former – bridges visual features to the LLM with 32 + 64 query tokens
  • LLM – Mistral-7B-Instruct-v0.2
  • Long-Term Attention – Gibbs-sampling-based continuous attention over basis functions for unbounded temporal context

File Overview

FileDescription
config.jsonHuggingFace AutoConfig configuration
configuration_videochat2.pyCustom PretrainedConfig subclass (Config)
videochat2_it_hd_mistral.pyMain model class (VideoChat2_it_hd_mistral)
blip2.pyBLIP-2 base class for vision-language bridging
vit.pyVision Transformer (UMT-L) implementation
Qformer.pyQ-Former module
basis_functions.pyBasis functions (Power, Sine, Cosine, Gaussian, Rectangular) for long-term attention
long_term_attention_gibbs.pyLong-term attention with Gibbs sampling
model-*.safetensorsModel weights (sharded, 4 parts)
.gitignoreIgnores model weight files

Usage

The model is registered with HuggingFace auto_map, so it can be loaded directly:

from transformers import AutoConfig, AutoModel

config = AutoConfig.from_pretrained("Rihong/VideoChat2_Infinity_Mistral_7B_hf", trust_remote_code=True)
model = AutoModel.from_pretrained("Rihong/VideoChat2_Infinity_Mistral_7B_hf", trust_remote_code=True)

To upload model to HuggingFace, just run:

hf upload Rihong/VideoChat2_Infinity_Mistral_7B_hf ./lmms_eval/baselines/infty_videochat2/

Key Hyperparameters

ParameterDefaultDescription
num_basis256Number of basis functions for long-term attention
tau0.75Temperature for Gibbs sampling
alpha0.75Mixing coefficient
stickytrueEnable sticky memories
hd_num6Number of high-definition crops
local_size224Local crop resolution

References

Fontes

1 fonte
Hugging FacePrimárioVersão c4f85eda8908f0e7e18f2eb093fe32e827b7df55
huggingface.co/Rihong/VideoChat2_HD_Infinity_Mistral_7B/tree/c4f85eda8908f0e7e18f2eb093fe32e827b7df55

Verificado 24 de out.

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Artefatos de modelo

4 artefatos

model-00001-of-00004.safetensors

safetensors · 4,63 GB · SHA-256 ecf6a804c5af…2e07 · Hugging Face

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model-00002-of-00004.safetensors

safetensors · 4,58 GB · SHA-256 bf5745ae7b32…0565 · Hugging Face

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model-00003-of-00004.safetensors

safetensors · 4,66 GB · SHA-256 475cbd791fe8…bc04 · Hugging Face

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model-00004-of-00004.safetensors

safetensors · 1,40 GB · SHA-256 7648e67deaaa…5d27 · Hugging Face

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Trechos de fonte

3 trechos

--- language: - en base_model: - mistralai/Mistral-7B-Instruct-v0.2 pipeline_tag: video-text-to-text library_name: transformers --- # Infinite-VideoChat2 HuggingFace-compatible model files for **VideoChat2-Infinity** (Mistral-7B), a long video understanding model that extends [VideoChat2-HD](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) with infinite-length video processing via continuous long-term attention. ## Model Architecture - **Vision Encoder** – UMT-L (ViT, 1024-dim, 24 layers) - **Q-Former** – bridges visual features to the LLM with 32 + 64 query tokens - **LLM** – [Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) - **Long-Term Attention** – Gibbs-sampling-based continuous attention over basis functions for unbounded temporal context ## File Overview | File | Description | |------|-------------| | `config.json` | HuggingFace `AutoConfig` configuration | | `configuration_videochat2.py` | Custom `PretrainedConfig` subclass (`Config`) | | `videochat2_it_hd_mistral.py` | Main model class (`VideoChat2_it_hd_mistral`) | | `blip2.py` | BLIP-2 base class for vision-language bridging | | `vit.py` | Vision Transformer (UMT-L) implementation | | `Qformer.py` | Q-Former module | | `basis_functions.py` | Basis functions (Power, Sine, Cosine, Gaussian, Rectangular) for long-term attention | | `long_term_attention_gibbs.py` | Long-term attention with Gibbs sampling | | `model-*.safetensors` | Model weights (sharded, 4 parts) | | `.gitignore` | Ignores model weight files | ## Usage The model is registered with HuggingFace `auto_map`, so it can be loaded directly: ```python from transformers import AutoConfig, AutoModel config = AutoConfig.from_pretrained("Rihong/VideoChat2_Infinity_Mistral_7B_hf", trust_remote_code=True) model = AutoModel.from_pretrained("Rihong/VideoChat2_Infinity_Mistral_7B_hf", trust_remote_code=True) ``` To upload model to HuggingFace, just run: ```bash hf upload Rihong/VideoChat2_Infinity_Mistral_7B_hf ./lmms_eval/baselines/infty_videochat2/ ``` ## Key Hyperparameters | Parameter | Default | Description | |-----------|---------|-------------| | `num_basis` | 256 | Number of basis functions for long-term attention | | `tau` | 0.75 | Temperature for Gibbs sampling | | `alpha` | 0.75 | Mixing coefficient | | `sticky` | `true` | Enable sticky memories | | `hd_num` | 6 | Number...

Source context: 18 downloads · 0 likes · Pipeline video-text-to-text · Library transformers · Repo Rihong/VideoChat2_HD_Infinity_Mistral_7B

Source context: 7 downloads · 0 likes · Pipeline video-text-to-text · Library transformers · Repo Rihong/VideoChat2_HD_Infinity_Mistral_7B