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README.md

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----
-license: apache-2.0
----
+---
+license: llama2
+---
+
+# Tarsier Model Card
+## Model details
+**Model type:** 
+Tarsier-7b is one of the Tarsier family -- an open-source large-scale video-language models, which is designed to generate high-quality video descriptions, together with good capability of general video understanding (Tarsier-34b gains SOTA results on 6 open benchmarks). Base LLM: [liuhaotian/llava-v1.6-vicuna-7b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b)
+
+**Model date:**
+Tarsier-7b was trained in June 2024.
+
+**Paper or resources for more information:**
+- github repo: https://github.com/bytedance/tarsier
+- paper link: https://arxiv.org/abs/2407.00634
+
+## License
+lmsys/vicuna-7b-v1.5 license.
+
+**Where to send questions or comments about the model:**
+https://github.com/bytedance/tarsier/issues
+
+## Intended use
+**Primary intended uses:**
+The primary use of Tarsier is research on large multimodal models, especially video description.
+
+**Primary intended users:**
+The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
+
+## Training dataset
+Tarsier tasks a two-stage training strategy.
+- Stage-1: Multi-task Pre-training on 13M data
+- Stage-2: Multi-grained Instruction Tuning on 500K data
+  
+In both stages, we freeze ViT and train all the parameters of projection layer and LLM.
+
+## Evaluation dataset
+- A challenging video desription dataset: [DREAM-1K](https://huggingface.co/datasets/omni-research/DREAM-1K)
+- Multi-choice VQA: [MVBench](https://huggingface.co/datasets/OpenGVLab/MVBench), [NeXT-QA](https://github.com/doc-doc/NExT-QA) and [Egoschema](https://drive.google.com/drive/folders/1SS0VVz8rML1e5gWq7D7VtP1oxE2UtmhQ)
+- Open-ended VQA: [MSVD-QA](https://opendatalab.com/OpenDataLab/MSVD), [MSR-VTT-QA](https://opendatalab.com/OpenDataLab/MSR-VTT), [ActivityNet-QA](https://github.com/MILVLG/activitynet-qa) and [TGIF-QA](https://opendatalab.com/OpenDataLab/TGIF-QA)
+- Video Caption: [MSVD-Caption](https://opendatalab.com/OpenDataLab/MSVD), [MSRVTT-Caption](https://opendatalab.com/OpenDataLab/MSR-VTT), [VATEX](https://eric-xw.github.io/vatex-website/about.html)
+
+## How to Use
+see https://github.com/bytedance/tarsier?tab=readme-ov-file#usage

added_tokens.json

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+{
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+}

config.json

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+{
+  "_name_or_path": "omni-research/Tarsier-7b",
+  "add_transformer_projector": false,
+  "architectures": [
+    "TarsierForConditionalGeneration"
+  ],
+  "ignore_index": -100,
+  "image_new_idx": 32003,
+  "image_newline_idx": 32002,
+  "image_token_index": 32000,
+  "model_type": "llava",
+  "projector_hidden_act": "gelu",
+  "text_config": {
+    "_name_or_path": "lmsys/vicuna-7b-v1.5",
+    "architectures": [
+      "LlamaForCausalLM"
+    ],
+    "max_position_embeddings": 4096,
+    "model_type": "llama",
+    "pad_token_id": 0,
+    "rms_norm_eps": 1e-05,
+    "torch_dtype": "float16",
+    "use_bpex_rmsnorm": true,
+    "use_bpex_rotary": true,
+    "use_cache": false,
+    "use_ft_flash_attn": true,
+    "vocab_size": 32064
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.37.2",
+  "vision_config": {
+    "hidden_size": 1024,
+    "image_size": 336,
+    "intermediate_size": 4096,
+    "model_type": "clip_vision_model",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768,
+    "vocab_size": 32000
+  },
+  "vision_feature_layer": -2,
+  "vision_feature_select_strategy": "default",
+  "vocab_size": 32064
+}

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