Clean up old pre-restructure paths (batch 1)
Browse filesThis view is limited to 50 files because it contains too many changes. See raw diff
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hf-mirror/Qwen/Qwen2.5-7B-Instruct/LICENSE
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defend, and hold each Contributor harmless for any liability
|
| 174 |
-
incurred by, or claims asserted against, such Contributor by reason
|
| 175 |
-
of your accepting any such warranty or additional liability.
|
| 176 |
-
|
| 177 |
-
END OF TERMS AND CONDITIONS
|
| 178 |
-
|
| 179 |
-
APPENDIX: How to apply the Apache License to your work.
|
| 180 |
-
|
| 181 |
-
To apply the Apache License to your work, attach the following
|
| 182 |
-
boilerplate notice, with the fields enclosed by brackets "[]"
|
| 183 |
-
replaced with your own identifying information. (Don't include
|
| 184 |
-
the brackets!) The text should be enclosed in the appropriate
|
| 185 |
-
comment syntax for the file format. We also recommend that a
|
| 186 |
-
file or class name and description of purpose be included on the
|
| 187 |
-
same "printed page" as the copyright notice for easier
|
| 188 |
-
identification within third-party archives.
|
| 189 |
-
|
| 190 |
-
Copyright 2024 Alibaba Cloud
|
| 191 |
-
|
| 192 |
-
Licensed under the Apache License, Version 2.0 (the "License");
|
| 193 |
-
you may not use this file except in compliance with the License.
|
| 194 |
-
You may obtain a copy of the License at
|
| 195 |
-
|
| 196 |
-
http://www.apache.org/licenses/LICENSE-2.0
|
| 197 |
-
|
| 198 |
-
Unless required by applicable law or agreed to in writing, software
|
| 199 |
-
distributed under the License is distributed on an "AS IS" BASIS,
|
| 200 |
-
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
| 201 |
-
See the License for the specific language governing permissions and
|
| 202 |
-
limitations under the License.
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|
hf-mirror/Qwen/Qwen2.5-7B-Instruct/README.md
DELETED
|
@@ -1,135 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
license: apache-2.0
|
| 3 |
-
license_link: https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/blob/main/LICENSE
|
| 4 |
-
language:
|
| 5 |
-
- en
|
| 6 |
-
pipeline_tag: text-generation
|
| 7 |
-
base_model: Qwen/Qwen2.5-7B
|
| 8 |
-
tags:
|
| 9 |
-
- chat
|
| 10 |
-
library_name: transformers
|
| 11 |
-
---
|
| 12 |
-
|
| 13 |
-
# Qwen2.5-7B-Instruct
|
| 14 |
-
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
|
| 15 |
-
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
|
| 16 |
-
</a>
|
| 17 |
-
|
| 18 |
-
## Introduction
|
| 19 |
-
|
| 20 |
-
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
|
| 21 |
-
|
| 22 |
-
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
|
| 23 |
-
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
|
| 24 |
-
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
|
| 25 |
-
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
|
| 26 |
-
|
| 27 |
-
**This repo contains the instruction-tuned 7B Qwen2.5 model**, which has the following features:
|
| 28 |
-
- Type: Causal Language Models
|
| 29 |
-
- Training Stage: Pretraining & Post-training
|
| 30 |
-
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
|
| 31 |
-
- Number of Parameters: 7.61B
|
| 32 |
-
- Number of Paramaters (Non-Embedding): 6.53B
|
| 33 |
-
- Number of Layers: 28
|
| 34 |
-
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
|
| 35 |
-
- Context Length: Full 131,072 tokens and generation 8192 tokens
|
| 36 |
-
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
|
| 37 |
-
|
| 38 |
-
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
|
| 39 |
-
|
| 40 |
-
## Requirements
|
| 41 |
-
|
| 42 |
-
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
|
| 43 |
-
|
| 44 |
-
With `transformers<4.37.0`, you will encounter the following error:
|
| 45 |
-
```
|
| 46 |
-
KeyError: 'qwen2'
|
| 47 |
-
```
|
| 48 |
-
|
| 49 |
-
## Quickstart
|
| 50 |
-
|
| 51 |
-
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
|
| 52 |
-
|
| 53 |
-
```python
|
| 54 |
-
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 55 |
-
|
| 56 |
-
model_name = "Qwen/Qwen2.5-7B-Instruct"
|
| 57 |
-
|
| 58 |
-
model = AutoModelForCausalLM.from_pretrained(
|
| 59 |
-
model_name,
|
| 60 |
-
torch_dtype="auto",
|
| 61 |
-
device_map="auto"
|
| 62 |
-
)
|
| 63 |
-
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 64 |
-
|
| 65 |
-
prompt = "Give me a short introduction to large language model."
|
| 66 |
-
messages = [
|
| 67 |
-
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
|
| 68 |
-
{"role": "user", "content": prompt}
|
| 69 |
-
]
|
| 70 |
-
text = tokenizer.apply_chat_template(
|
| 71 |
-
messages,
|
| 72 |
-
tokenize=False,
|
| 73 |
-
add_generation_prompt=True
|
| 74 |
-
)
|
| 75 |
-
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
|
| 76 |
-
|
| 77 |
-
generated_ids = model.generate(
|
| 78 |
-
**model_inputs,
|
| 79 |
-
max_new_tokens=512
|
| 80 |
-
)
|
| 81 |
-
generated_ids = [
|
| 82 |
-
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
|
| 83 |
-
]
|
| 84 |
-
|
| 85 |
-
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
|
| 86 |
-
```
|
| 87 |
-
|
| 88 |
-
### Processing Long Texts
|
| 89 |
-
|
| 90 |
-
The current `config.json` is set for context length up to 32,768 tokens.
|
| 91 |
-
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
|
| 92 |
-
|
| 93 |
-
For supported frameworks, you could add the following to `config.json` to enable YaRN:
|
| 94 |
-
```json
|
| 95 |
-
{
|
| 96 |
-
...,
|
| 97 |
-
"rope_scaling": {
|
| 98 |
-
"factor": 4.0,
|
| 99 |
-
"original_max_position_embeddings": 32768,
|
| 100 |
-
"type": "yarn"
|
| 101 |
-
}
|
| 102 |
-
}
|
| 103 |
-
```
|
| 104 |
-
|
| 105 |
-
For deployment, we recommend using vLLM.
|
| 106 |
-
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
|
| 107 |
-
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
|
| 108 |
-
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
|
| 109 |
-
|
| 110 |
-
## Evaluation & Performance
|
| 111 |
-
|
| 112 |
-
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
|
| 113 |
-
|
| 114 |
-
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
|
| 115 |
-
|
| 116 |
-
## Citation
|
| 117 |
-
|
| 118 |
-
If you find our work helpful, feel free to give us a cite.
|
| 119 |
-
|
| 120 |
-
```
|
| 121 |
-
@misc{qwen2.5,
|
| 122 |
-
title = {Qwen2.5: A Party of Foundation Models},
|
| 123 |
-
url = {https://qwenlm.github.io/blog/qwen2.5/},
|
| 124 |
-
author = {Qwen Team},
|
| 125 |
-
month = {September},
|
| 126 |
-
year = {2024}
|
| 127 |
-
}
|
| 128 |
-
|
| 129 |
-
@article{qwen2,
|
| 130 |
-
title={Qwen2 Technical Report},
|
| 131 |
-
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
|
| 132 |
-
journal={arXiv preprint arXiv:2407.10671},
|
| 133 |
-
year={2024}
|
| 134 |
-
}
|
| 135 |
-
```
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|
hf-mirror/Qwen/Qwen2.5-7B-Instruct/config.json
DELETED
|
@@ -1,27 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"architectures": [
|
| 3 |
-
"Qwen2ForCausalLM"
|
| 4 |
-
],
|
| 5 |
-
"attention_dropout": 0.0,
|
| 6 |
-
"bos_token_id": 151643,
|
| 7 |
-
"eos_token_id": 151645,
|
| 8 |
-
"hidden_act": "silu",
|
| 9 |
-
"hidden_size": 3584,
|
| 10 |
-
"initializer_range": 0.02,
|
| 11 |
-
"intermediate_size": 18944,
|
| 12 |
-
"max_position_embeddings": 32768,
|
| 13 |
-
"max_window_layers": 28,
|
| 14 |
-
"model_type": "qwen2",
|
| 15 |
-
"num_attention_heads": 28,
|
| 16 |
-
"num_hidden_layers": 28,
|
| 17 |
-
"num_key_value_heads": 4,
|
| 18 |
-
"rms_norm_eps": 1e-06,
|
| 19 |
-
"rope_theta": 1000000.0,
|
| 20 |
-
"sliding_window": 131072,
|
| 21 |
-
"tie_word_embeddings": false,
|
| 22 |
-
"torch_dtype": "bfloat16",
|
| 23 |
-
"transformers_version": "4.43.1",
|
| 24 |
-
"use_cache": true,
|
| 25 |
-
"use_sliding_window": false,
|
| 26 |
-
"vocab_size": 152064
|
| 27 |
-
}
|
|
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|
hf-mirror/Qwen/Qwen2.5-7B-Instruct/generation_config.json
DELETED
|
@@ -1,14 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"bos_token_id": 151643,
|
| 3 |
-
"pad_token_id": 151643,
|
| 4 |
-
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|
hf-mirror/Qwen/Qwen2.5-7B-Instruct/tokenizer.json
DELETED
|
The diff for this file is too large to render.
See raw diff
|
|
|
hf-mirror/Qwen/Qwen2.5-7B-Instruct/tokenizer_config.json
DELETED
|
@@ -1,207 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"add_bos_token": false,
|
| 3 |
-
"add_prefix_space": false,
|
| 4 |
-
"added_tokens_decoder": {
|
| 5 |
-
"151643": {
|
| 6 |
-
"content": "<|endoftext|>",
|
| 7 |
-
"lstrip": false,
|
| 8 |
-
"normalized": false,
|
| 9 |
-
"rstrip": false,
|
| 10 |
-
"single_word": false,
|
| 11 |
-
"special": true
|
| 12 |
-
},
|
| 13 |
-
"151644": {
|
| 14 |
-
"content": "<|im_start|>",
|
| 15 |
-
"lstrip": false,
|
| 16 |
-
"normalized": false,
|
| 17 |
-
"rstrip": false,
|
| 18 |
-
"single_word": false,
|
| 19 |
-
"special": true
|
| 20 |
-
},
|
| 21 |
-
"151645": {
|
| 22 |
-
"content": "<|im_end|>",
|
| 23 |
-
"lstrip": false,
|
| 24 |
-
"normalized": false,
|
| 25 |
-
"rstrip": false,
|
| 26 |
-
"single_word": false,
|
| 27 |
-
"special": true
|
| 28 |
-
},
|
| 29 |
-
"151646": {
|
| 30 |
-
"content": "<|object_ref_start|>",
|
| 31 |
-
"lstrip": false,
|
| 32 |
-
"normalized": false,
|
| 33 |
-
"rstrip": false,
|
| 34 |
-
"single_word": false,
|
| 35 |
-
"special": true
|
| 36 |
-
},
|
| 37 |
-
"151647": {
|
| 38 |
-
"content": "<|object_ref_end|>",
|
| 39 |
-
"lstrip": false,
|
| 40 |
-
"normalized": false,
|
| 41 |
-
"rstrip": false,
|
| 42 |
-
"single_word": false,
|
| 43 |
-
"special": true
|
| 44 |
-
},
|
| 45 |
-
"151648": {
|
| 46 |
-
"content": "<|box_start|>",
|
| 47 |
-
"lstrip": false,
|
| 48 |
-
"normalized": false,
|
| 49 |
-
"rstrip": false,
|
| 50 |
-
"single_word": false,
|
| 51 |
-
"special": true
|
| 52 |
-
},
|
| 53 |
-
"151649": {
|
| 54 |
-
"content": "<|box_end|>",
|
| 55 |
-
"lstrip": false,
|
| 56 |
-
"normalized": false,
|
| 57 |
-
"rstrip": false,
|
| 58 |
-
"single_word": false,
|
| 59 |
-
"special": true
|
| 60 |
-
},
|
| 61 |
-
"151650": {
|
| 62 |
-
"content": "<|quad_start|>",
|
| 63 |
-
"lstrip": false,
|
| 64 |
-
"normalized": false,
|
| 65 |
-
"rstrip": false,
|
| 66 |
-
"single_word": false,
|
| 67 |
-
"special": true
|
| 68 |
-
},
|
| 69 |
-
"151651": {
|
| 70 |
-
"content": "<|quad_end|>",
|
| 71 |
-
"lstrip": false,
|
| 72 |
-
"normalized": false,
|
| 73 |
-
"rstrip": false,
|
| 74 |
-
"single_word": false,
|
| 75 |
-
"special": true
|
| 76 |
-
},
|
| 77 |
-
"151652": {
|
| 78 |
-
"content": "<|vision_start|>",
|
| 79 |
-
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|
| 80 |
-
"normalized": false,
|
| 81 |
-
"rstrip": false,
|
| 82 |
-
"single_word": false,
|
| 83 |
-
"special": true
|
| 84 |
-
},
|
| 85 |
-
"151653": {
|
| 86 |
-
"content": "<|vision_end|>",
|
| 87 |
-
"lstrip": false,
|
| 88 |
-
"normalized": false,
|
| 89 |
-
"rstrip": false,
|
| 90 |
-
"single_word": false,
|
| 91 |
-
"special": true
|
| 92 |
-
},
|
| 93 |
-
"151654": {
|
| 94 |
-
"content": "<|vision_pad|>",
|
| 95 |
-
"lstrip": false,
|
| 96 |
-
"normalized": false,
|
| 97 |
-
"rstrip": false,
|
| 98 |
-
"single_word": false,
|
| 99 |
-
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|
| 100 |
-
},
|
| 101 |
-
"151655": {
|
| 102 |
-
"content": "<|image_pad|>",
|
| 103 |
-
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|
| 104 |
-
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|
| 105 |
-
"rstrip": false,
|
| 106 |
-
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|
| 107 |
-
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|
| 108 |
-
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| 109 |
-
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|
| 110 |
-
"content": "<|video_pad|>",
|
| 111 |
-
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|
| 112 |
-
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|
| 113 |
-
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|
| 114 |
-
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|
| 115 |
-
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|
| 116 |
-
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|
| 117 |
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|
| 118 |
-
"content": "<tool_call>",
|
| 119 |
-
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|
| 120 |
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| 121 |
-
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|
| 122 |
-
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|
| 123 |
-
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|
| 124 |
-
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| 125 |
-
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|
| 126 |
-
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|
| 127 |
-
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|
| 128 |
-
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|
| 129 |
-
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|
| 130 |
-
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|
| 131 |
-
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|
| 132 |
-
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| 133 |
-
"151659": {
|
| 134 |
-
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|
| 135 |
-
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|
| 136 |
-
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|
| 137 |
-
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|
| 138 |
-
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|
| 139 |
-
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|
| 140 |
-
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|
| 141 |
-
"151660": {
|
| 142 |
-
"content": "<|fim_middle|>",
|
| 143 |
-
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|
| 144 |
-
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|
| 145 |
-
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|
| 146 |
-
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|
| 147 |
-
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|
| 148 |
-
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| 149 |
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|
| 150 |
-
"content": "<|fim_suffix|>",
|
| 151 |
-
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|
| 152 |
-
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|
| 153 |
-
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|
| 154 |
-
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|
| 155 |
-
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|
| 156 |
-
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|
| 157 |
-
"151662": {
|
| 158 |
-
"content": "<|fim_pad|>",
|
| 159 |
-
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|
| 160 |
-
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| 161 |
-
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| 162 |
-
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|
| 163 |
-
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| 164 |
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| 165 |
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| 166 |
-
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| 167 |
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| 168 |
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| 169 |
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| 170 |
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| 171 |
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| 172 |
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| 173 |
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| 174 |
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| 175 |
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| 176 |
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| 177 |
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| 178 |
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|
| 179 |
-
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|
| 180 |
-
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|
| 181 |
-
},
|
| 182 |
-
"additional_special_tokens": [
|
| 183 |
-
"<|im_start|>",
|
| 184 |
-
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|
| 185 |
-
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|
| 186 |
-
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|
| 187 |
-
"<|box_start|>",
|
| 188 |
-
"<|box_end|>",
|
| 189 |
-
"<|quad_start|>",
|
| 190 |
-
"<|quad_end|>",
|
| 191 |
-
"<|vision_start|>",
|
| 192 |
-
"<|vision_end|>",
|
| 193 |
-
"<|vision_pad|>",
|
| 194 |
-
"<|image_pad|>",
|
| 195 |
-
"<|video_pad|>"
|
| 196 |
-
],
|
| 197 |
-
"bos_token": null,
|
| 198 |
-
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
| 199 |
-
"clean_up_tokenization_spaces": false,
|
| 200 |
-
"eos_token": "<|im_end|>",
|
| 201 |
-
"errors": "replace",
|
| 202 |
-
"model_max_length": 131072,
|
| 203 |
-
"pad_token": "<|endoftext|>",
|
| 204 |
-
"split_special_tokens": false,
|
| 205 |
-
"tokenizer_class": "Qwen2Tokenizer",
|
| 206 |
-
"unk_token": null
|
| 207 |
-
}
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hf-mirror/Qwen/Qwen2.5-7B-Instruct/vocab.json
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|
The diff for this file is too large to render.
See raw diff
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|
|
hf-mirror/liuhaotian/llava-v1.6-34b/.gitattributes
DELETED
|
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|
| 1 |
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*.7z filter=lfs diff=lfs merge=lfs -text
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| 7 |
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*.gz filter=lfs diff=lfs merge=lfs -text
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| 8 |
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*.h5 filter=lfs diff=lfs merge=lfs -text
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| 9 |
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*.joblib filter=lfs diff=lfs merge=lfs -text
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| 10 |
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
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| 12 |
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*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
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*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
-
*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
-
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
-
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
-
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
-
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
-
*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
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*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
-
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
-
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
-
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
-
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
-
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
-
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
-
*.tar filter=lfs diff=lfs merge=lfs -text
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| 29 |
-
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 30 |
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*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 31 |
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*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 32 |
-
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 33 |
-
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
-
*tfevents* filter=lfs diff=lfs merge=lfs -text
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hf-mirror/liuhaotian/llava-v1.6-34b/README.md
DELETED
|
@@ -1,49 +0,0 @@
|
|
| 1 |
-
---
|
| 2 |
-
inference: false
|
| 3 |
-
license: apache-2.0
|
| 4 |
-
library_name: LLaVA
|
| 5 |
-
pipeline_tag: image-text-to-text
|
| 6 |
-
tags:
|
| 7 |
-
- llava
|
| 8 |
-
---
|
| 9 |
-
|
| 10 |
-
<br>
|
| 11 |
-
<br>
|
| 12 |
-
|
| 13 |
-
# LLaVA Model Card
|
| 14 |
-
|
| 15 |
-
## Model details
|
| 16 |
-
|
| 17 |
-
**Model type:**
|
| 18 |
-
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
|
| 19 |
-
It is an auto-regressive language model, based on the transformer architecture.
|
| 20 |
-
Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)
|
| 21 |
-
|
| 22 |
-
**Model date:**
|
| 23 |
-
LLaVA-v1.6-34B was trained in December 2023.
|
| 24 |
-
|
| 25 |
-
**Paper or resources for more information:**
|
| 26 |
-
https://llava-vl.github.io/
|
| 27 |
-
|
| 28 |
-
## License
|
| 29 |
-
[NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license.
|
| 30 |
-
|
| 31 |
-
**Where to send questions or comments about the model:**
|
| 32 |
-
https://github.com/haotian-liu/LLaVA/issues
|
| 33 |
-
|
| 34 |
-
## Intended use
|
| 35 |
-
**Primary intended uses:**
|
| 36 |
-
The primary use of LLaVA is research on large multimodal models and chatbots.
|
| 37 |
-
|
| 38 |
-
**Primary intended users:**
|
| 39 |
-
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
|
| 40 |
-
|
| 41 |
-
## Training dataset
|
| 42 |
-
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
| 43 |
-
- 158K GPT-generated multimodal instruction-following data.
|
| 44 |
-
- 500K academic-task-oriented VQA data mixture.
|
| 45 |
-
- 50K GPT-4V data mixture.
|
| 46 |
-
- 40K ShareGPT data.
|
| 47 |
-
|
| 48 |
-
## Evaluation dataset
|
| 49 |
-
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.
|
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|
hf-mirror/liuhaotian/llava-v1.6-34b/config.json
DELETED
|
@@ -1,74 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"_name_or_path": "./checkpoints/Nous-Hermes-2-Yi-34B",
|
| 3 |
-
"architectures": [
|
| 4 |
-
"LlavaLlamaForCausalLM"
|
| 5 |
-
],
|
| 6 |
-
"attention_bias": false,
|
| 7 |
-
"attention_dropout": 0.0,
|
| 8 |
-
"bos_token_id": 1,
|
| 9 |
-
"eos_token_id": 7,
|
| 10 |
-
"freeze_mm_mlp_adapter": false,
|
| 11 |
-
"freeze_mm_vision_resampler": false,
|
| 12 |
-
"hidden_act": "silu",
|
| 13 |
-
"hidden_size": 7168,
|
| 14 |
-
"image_aspect_ratio": "anyres",
|
| 15 |
-
"image_crop_resolution": 224,
|
| 16 |
-
"image_grid_pinpoints": [
|
| 17 |
-
[
|
| 18 |
-
336,
|
| 19 |
-
672
|
| 20 |
-
],
|
| 21 |
-
[
|
| 22 |
-
672,
|
| 23 |
-
336
|
| 24 |
-
],
|
| 25 |
-
[
|
| 26 |
-
672,
|
| 27 |
-
672
|
| 28 |
-
],
|
| 29 |
-
[
|
| 30 |
-
1008,
|
| 31 |
-
336
|
| 32 |
-
],
|
| 33 |
-
[
|
| 34 |
-
336,
|
| 35 |
-
1008
|
| 36 |
-
]
|
| 37 |
-
],
|
| 38 |
-
"image_split_resolution": 224,
|
| 39 |
-
"initializer_range": 0.02,
|
| 40 |
-
"intermediate_size": 20480,
|
| 41 |
-
"max_position_embeddings": 4096,
|
| 42 |
-
"mm_hidden_size": 1024,
|
| 43 |
-
"mm_patch_merge_type": "spatial_unpad",
|
| 44 |
-
"mm_projector_lr": null,
|
| 45 |
-
"mm_projector_type": "mlp2x_gelu",
|
| 46 |
-
"mm_resampler_type": null,
|
| 47 |
-
"mm_use_im_patch_token": false,
|
| 48 |
-
"mm_use_im_start_end": false,
|
| 49 |
-
"mm_vision_select_feature": "patch",
|
| 50 |
-
"mm_vision_select_layer": -2,
|
| 51 |
-
"mm_vision_tower": "openai/clip-vit-large-patch14-336",
|
| 52 |
-
"mm_vision_tower_lr": 2e-06,
|
| 53 |
-
"model_type": "llava",
|
| 54 |
-
"num_attention_heads": 56,
|
| 55 |
-
"num_hidden_layers": 60,
|
| 56 |
-
"num_key_value_heads": 8,
|
| 57 |
-
"pad_token_id": 0,
|
| 58 |
-
"pretraining_tp": 1,
|
| 59 |
-
"rms_norm_eps": 1e-05,
|
| 60 |
-
"rope_scaling": null,
|
| 61 |
-
"rope_theta": 5000000.0,
|
| 62 |
-
"tie_word_embeddings": false,
|
| 63 |
-
"tokenizer_model_max_length": 4096,
|
| 64 |
-
"tokenizer_padding_side": "right",
|
| 65 |
-
"torch_dtype": "bfloat16",
|
| 66 |
-
"transformers_version": "4.36.2",
|
| 67 |
-
"tune_mm_mlp_adapter": false,
|
| 68 |
-
"tune_mm_vision_resampler": false,
|
| 69 |
-
"unfreeze_mm_vision_tower": true,
|
| 70 |
-
"use_cache": true,
|
| 71 |
-
"use_mm_proj": true,
|
| 72 |
-
"vocab_size": 64000,
|
| 73 |
-
"image_token_index": 64002
|
| 74 |
-
}
|
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hf-mirror/liuhaotian/llava-v1.6-34b/special_tokens_map.json
DELETED
|
@@ -1,24 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"bos_token": {
|
| 3 |
-
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| 4 |
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|
| 15 |
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| 16 |
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| 18 |
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hf-mirror/liuhaotian/llava-v1.6-34b/tokenizer.model
DELETED
|
@@ -1,3 +0,0 @@
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|
| 1 |
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| 2 |
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hf-mirror/liuhaotian/llava-v1.6-34b/tokenizer_config.json
DELETED
|
@@ -1,61 +0,0 @@
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| 1 |
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|
| 2 |
-
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| 3 |
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| 17 |
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| 27 |
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| 28 |
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| 30 |
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|
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| 47 |
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| 48 |
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|
| 49 |
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| 50 |
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|
| 51 |
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|
| 52 |
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| 55 |
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| 56 |
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| 57 |
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|
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| 60 |
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/.gitattributes
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/README.md
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|
| 1 |
-
---
|
| 2 |
-
datasets:
|
| 3 |
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- lmms-lab/LLaVA-OneVision-Data
|
| 4 |
-
- lmms-lab/LLaVA-Video-178K
|
| 5 |
-
language:
|
| 6 |
-
- en
|
| 7 |
-
library_name: transformers
|
| 8 |
-
license: apache-2.0
|
| 9 |
-
metrics:
|
| 10 |
-
- accuracy
|
| 11 |
-
tags:
|
| 12 |
-
- multimodal
|
| 13 |
-
pipeline_tag: video-text-to-text
|
| 14 |
-
model-index:
|
| 15 |
-
- name: LLaVA-Video-7B-Qwen2
|
| 16 |
-
results:
|
| 17 |
-
- task:
|
| 18 |
-
type: multimodal
|
| 19 |
-
dataset:
|
| 20 |
-
name: ActNet-QA
|
| 21 |
-
type: actnet-qa
|
| 22 |
-
metrics:
|
| 23 |
-
- type: accuracy
|
| 24 |
-
value: 56.5
|
| 25 |
-
name: accuracy
|
| 26 |
-
verified: true
|
| 27 |
-
- task:
|
| 28 |
-
type: multimodal
|
| 29 |
-
dataset:
|
| 30 |
-
name: EgoSchema
|
| 31 |
-
type: egoschema
|
| 32 |
-
metrics:
|
| 33 |
-
- type: accuracy
|
| 34 |
-
value: 57.3
|
| 35 |
-
name: accuracy
|
| 36 |
-
verified: true
|
| 37 |
-
- task:
|
| 38 |
-
type: multimodal
|
| 39 |
-
dataset:
|
| 40 |
-
name: MLVU
|
| 41 |
-
type: mlvu
|
| 42 |
-
metrics:
|
| 43 |
-
- type: accuracy
|
| 44 |
-
value: 70.8
|
| 45 |
-
name: accuracy
|
| 46 |
-
verified: true
|
| 47 |
-
- task:
|
| 48 |
-
type: multimodal
|
| 49 |
-
dataset:
|
| 50 |
-
name: MVBench
|
| 51 |
-
type: mvbench
|
| 52 |
-
metrics:
|
| 53 |
-
- type: accuracy
|
| 54 |
-
value: 58.6
|
| 55 |
-
name: accuracy
|
| 56 |
-
verified: true
|
| 57 |
-
- task:
|
| 58 |
-
type: multimodal
|
| 59 |
-
dataset:
|
| 60 |
-
name: NextQA
|
| 61 |
-
type: nextqa
|
| 62 |
-
metrics:
|
| 63 |
-
- type: accuracy
|
| 64 |
-
value: 83.2
|
| 65 |
-
name: accuracy
|
| 66 |
-
verified: true
|
| 67 |
-
- task:
|
| 68 |
-
type: multimodal
|
| 69 |
-
dataset:
|
| 70 |
-
name: PercepTest
|
| 71 |
-
type: percepTest
|
| 72 |
-
metrics:
|
| 73 |
-
- type: accuracy
|
| 74 |
-
value: 67.9
|
| 75 |
-
name: accuracy
|
| 76 |
-
verified: true
|
| 77 |
-
- task:
|
| 78 |
-
type: multimodal
|
| 79 |
-
dataset:
|
| 80 |
-
name: VideoChatGPT
|
| 81 |
-
type: videochatgpt
|
| 82 |
-
metrics:
|
| 83 |
-
- type: score
|
| 84 |
-
value: 3.52
|
| 85 |
-
name: score
|
| 86 |
-
verified: true
|
| 87 |
-
- task:
|
| 88 |
-
type: multimodal
|
| 89 |
-
dataset:
|
| 90 |
-
name: VideoDC
|
| 91 |
-
type: videodc
|
| 92 |
-
metrics:
|
| 93 |
-
- type: score
|
| 94 |
-
value: 3.66
|
| 95 |
-
name: score
|
| 96 |
-
verified: true
|
| 97 |
-
- task:
|
| 98 |
-
type: multimodal
|
| 99 |
-
dataset:
|
| 100 |
-
name: LongVideoBench
|
| 101 |
-
type: longvideobench
|
| 102 |
-
metrics:
|
| 103 |
-
- type: accuracy
|
| 104 |
-
value: 58.2
|
| 105 |
-
name: accuracy
|
| 106 |
-
verified: true
|
| 107 |
-
- task:
|
| 108 |
-
type: multimodal
|
| 109 |
-
dataset:
|
| 110 |
-
name: VideoMME
|
| 111 |
-
type: videomme
|
| 112 |
-
metrics:
|
| 113 |
-
- type: accuracy
|
| 114 |
-
value: 63.3
|
| 115 |
-
name: accuracy
|
| 116 |
-
verified: true
|
| 117 |
-
base_model:
|
| 118 |
-
- lmms-lab/llava-onevision-qwen2-7b-si
|
| 119 |
-
---
|
| 120 |
-
|
| 121 |
-
# LLaVA-Video-7B-Qwen2
|
| 122 |
-
|
| 123 |
-
## Table of Contents
|
| 124 |
-
|
| 125 |
-
1. [Model Summary](##model-summary)
|
| 126 |
-
2. [Use](##use)
|
| 127 |
-
3. [Limitations](##limitations)
|
| 128 |
-
4. [Training](##training)
|
| 129 |
-
5. [License](##license)
|
| 130 |
-
6. [Citation](##citation)
|
| 131 |
-
|
| 132 |
-
## Model Summary
|
| 133 |
-
|
| 134 |
-
The LLaVA-Video models are 7/72B parameter models trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), based on Qwen2 language model with a context window of 32K tokens.
|
| 135 |
-
|
| 136 |
-
This model support at most 64 frames.
|
| 137 |
-
|
| 138 |
-
- **Project Page:** [Project Page](https://llava-vl.github.io/blog/2024-09-30-llava-video/).
|
| 139 |
-
- **Paper**: For more details, please check our [paper](https://arxiv.org/abs/2410.02713)
|
| 140 |
-
- **Repository:** [LLaVA-VL/LLaVA-NeXT](https://github.com/LLaVA-VL/LLaVA-NeXT?tab=readme-ov-file)
|
| 141 |
-
- **Point of Contact:** [Yuanhan Zhang](https://zhangyuanhan-ai.github.io/)
|
| 142 |
-
- **Languages:** English, Chinese
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
## Use
|
| 146 |
-
|
| 147 |
-
### Intended use
|
| 148 |
-
|
| 149 |
-
The model was trained on [LLaVA-Video-178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K) and [LLaVA-OneVision Dataset](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), having the ability to interact with images, multi-image and videos, but specific to videos.
|
| 150 |
-
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
**Feel free to share your generations in the Community tab!**
|
| 154 |
-
|
| 155 |
-
### Generation
|
| 156 |
-
|
| 157 |
-
We provide the simple generation process for using our model. For more details, you could refer to [Github](https://github.com/LLaVA-VL/LLaVA-NeXT).
|
| 158 |
-
|
| 159 |
-
```python
|
| 160 |
-
# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
|
| 161 |
-
from llava.model.builder import load_pretrained_model
|
| 162 |
-
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
|
| 163 |
-
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
|
| 164 |
-
from llava.conversation import conv_templates, SeparatorStyle
|
| 165 |
-
from PIL import Image
|
| 166 |
-
import requests
|
| 167 |
-
import copy
|
| 168 |
-
import torch
|
| 169 |
-
import sys
|
| 170 |
-
import warnings
|
| 171 |
-
from decord import VideoReader, cpu
|
| 172 |
-
import numpy as np
|
| 173 |
-
warnings.filterwarnings("ignore")
|
| 174 |
-
def load_video(video_path, max_frames_num,fps=1,force_sample=False):
|
| 175 |
-
if max_frames_num == 0:
|
| 176 |
-
return np.zeros((1, 336, 336, 3))
|
| 177 |
-
vr = VideoReader(video_path, ctx=cpu(0),num_threads=1)
|
| 178 |
-
total_frame_num = len(vr)
|
| 179 |
-
video_time = total_frame_num / vr.get_avg_fps()
|
| 180 |
-
fps = round(vr.get_avg_fps()/fps)
|
| 181 |
-
frame_idx = [i for i in range(0, len(vr), fps)]
|
| 182 |
-
frame_time = [i/fps for i in frame_idx]
|
| 183 |
-
if len(frame_idx) > max_frames_num or force_sample:
|
| 184 |
-
sample_fps = max_frames_num
|
| 185 |
-
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, sample_fps, dtype=int)
|
| 186 |
-
frame_idx = uniform_sampled_frames.tolist()
|
| 187 |
-
frame_time = [i/vr.get_avg_fps() for i in frame_idx]
|
| 188 |
-
frame_time = ",".join([f"{i:.2f}s" for i in frame_time])
|
| 189 |
-
spare_frames = vr.get_batch(frame_idx).asnumpy()
|
| 190 |
-
# import pdb;pdb.set_trace()
|
| 191 |
-
return spare_frames,frame_time,video_time
|
| 192 |
-
pretrained = "lmms-lab/LLaVA-Video-7B-Qwen2"
|
| 193 |
-
model_name = "llava_qwen"
|
| 194 |
-
device = "cuda"
|
| 195 |
-
device_map = "auto"
|
| 196 |
-
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, torch_dtype="bfloat16", device_map=device_map) # Add any other thing you want to pass in llava_model_args
|
| 197 |
-
model.eval()
|
| 198 |
-
video_path = "XXXX"
|
| 199 |
-
max_frames_num = 64
|
| 200 |
-
video,frame_time,video_time = load_video(video_path, max_frames_num, 1, force_sample=True)
|
| 201 |
-
video = image_processor.preprocess(video, return_tensors="pt")["pixel_values"].cuda().half()
|
| 202 |
-
video = [video]
|
| 203 |
-
conv_template = "qwen_1_5" # Make sure you use correct chat template for different models
|
| 204 |
-
time_instruciton = f"The video lasts for {video_time:.2f} seconds, and {len(video[0])} frames are uniformly sampled from it. These frames are located at {frame_time}.Please answer the following questions related to this video."
|
| 205 |
-
question = DEFAULT_IMAGE_TOKEN + f"\n{time_instruciton}\nPlease describe this video in detail."
|
| 206 |
-
conv = copy.deepcopy(conv_templates[conv_template])
|
| 207 |
-
conv.append_message(conv.roles[0], question)
|
| 208 |
-
conv.append_message(conv.roles[1], None)
|
| 209 |
-
prompt_question = conv.get_prompt()
|
| 210 |
-
input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(device)
|
| 211 |
-
cont = model.generate(
|
| 212 |
-
input_ids,
|
| 213 |
-
images=video,
|
| 214 |
-
modalities= ["video"],
|
| 215 |
-
do_sample=False,
|
| 216 |
-
temperature=0,
|
| 217 |
-
max_new_tokens=4096,
|
| 218 |
-
)
|
| 219 |
-
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip()
|
| 220 |
-
print(text_outputs)
|
| 221 |
-
```
|
| 222 |
-
|
| 223 |
-
|
| 224 |
-
# Training
|
| 225 |
-
|
| 226 |
-
## Model
|
| 227 |
-
|
| 228 |
-
- **Architecture:** SO400M + Qwen2
|
| 229 |
-
- **Initialized Model:** lmms-lab/llava-onevision-qwen2-7b-si
|
| 230 |
-
- **Data:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
|
| 231 |
-
- **Precision:** bfloat16
|
| 232 |
-
|
| 233 |
-
## Hardware & Software
|
| 234 |
-
|
| 235 |
-
- **GPUs:** 256 * Nvidia Tesla A100 (for whole model series training)
|
| 236 |
-
- **Orchestration:** [Huggingface Trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
|
| 237 |
-
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
|
| 238 |
-
|
| 239 |
-
# Citation
|
| 240 |
-
|
| 241 |
-
```bibtex
|
| 242 |
-
|
| 243 |
-
@misc{zhang2024videoinstructiontuningsynthetic,
|
| 244 |
-
title={Video Instruction Tuning With Synthetic Data},
|
| 245 |
-
author={Yuanhan Zhang and Jinming Wu and Wei Li and Bo Li and Zejun Ma and Ziwei Liu and Chunyuan Li},
|
| 246 |
-
year={2024},
|
| 247 |
-
eprint={2410.02713},
|
| 248 |
-
archivePrefix={arXiv},
|
| 249 |
-
primaryClass={cs.CV},
|
| 250 |
-
url={https://arxiv.org/abs/2410.02713},
|
| 251 |
-
}
|
| 252 |
-
```
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/added_tokens.json
DELETED
|
@@ -1,6 +0,0 @@
|
|
| 1 |
-
{
|
| 2 |
-
"<image>": 151646,
|
| 3 |
-
"<|endoftext|>": 151643,
|
| 4 |
-
"<|im_end|>": 151645,
|
| 5 |
-
"<|im_start|>": 151644
|
| 6 |
-
}
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/config.json
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/generation_config.json
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{
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/merges.txt
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/model-00001-of-00004.safetensors
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hf-mirror/lmms-lab/LLaVA-Video-7B-Qwen2/model-00002-of-00004.safetensors
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