初始化项目，由ModelHub XC社区提供模型
Model: Vchitect/ShotVL-3B Source: Original Platform
This commit is contained in:
ModelHub XC
36
.gitattributes
vendored
Normal file
36
.gitattributes
vendored
Normal file
@@ -0,0 +1,36 @@
|
|||||||
|
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.model filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||||
|
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||||
|
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||||
|
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
||||||
235
README.md
Normal file
235
README.md
Normal file
@@ -0,0 +1,235 @@
|
|||||||
|
---
|
||||||
|
base_model:
|
||||||
|
- Qwen/Qwen2.5-VL-3B-Instruct
|
||||||
|
license: apache-2.0
|
||||||
|
tags:
|
||||||
|
- vision-language
|
||||||
|
- cinematography
|
||||||
|
- shotbench
|
||||||
|
pipeline_tag: image-text-to-text
|
||||||
|
library_name: transformers
|
||||||
|
---
|
||||||
|
|
||||||
|
# ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
|
||||||
|
|
||||||
|
This repository contains **ShotVL-3B**, a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct), developed for expert-level cinematic understanding.
|
||||||
|
|
||||||
|
* **Paper:** [ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models](https://arxiv.org/abs/2506.21356)
|
||||||
|
* **Project Page:** [https://vchitect.github.io/ShotBench-project/](https://vchitect.github.io/ShotBench-project/)
|
||||||
|
* **Code:** [https://github.com/Vchitect/ShotBench](https://github.com/Vchitect/ShotBench)
|
||||||
|
|
||||||
|
## Abstract
|
||||||
|
|
||||||
|
Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrate strong general visual understanding, their proficiency in comprehending the nuanced cinematic grammar embedded within individual shots remains largely unexplored and lacks robust evaluation. This critical gap limits both fine-grained visual comprehension and the precision of AI-assisted video generation. To address this, we introduce ShotBench, a comprehensive benchmark specifically designed for cinematic language understanding. It features over 3.5k expert-annotated QA pairs from images and video clips, meticulously curated from over 200 acclaimed (predominantly Oscar-nominated) films and spanning eight key cinematography dimensions. Our evaluation of 24 leading VLMs on ShotBench reveals their substantial limitations: even the top-performing model achieves less than 60% average accuracy, particularly struggling with fine-grained visual cues and complex spatial reasoning. To catalyze advancement in this domain, we construct ShotQA, a large-scale multimodal dataset comprising approximately 70k cinematic QA pairs. Leveraging ShotQA, we develop ShotVL through supervised fine-tuning and Group Relative Policy Optimization. ShotVL significantly outperforms all existing open-source and proprietary models on ShotBench, establishing new state-of-the-art performance. We open-source our models, data, and code to foster rapid progress in this crucial area of AI-driven cinematic understanding and generation.
|
||||||
|
|
||||||
|
## Model description
|
||||||
|
|
||||||
|
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct), trained by supervised fine-tuning and GRPO on the largest and high-quality dataset for cinematic language understanding to date. It currently achieves state-of-the-art performance on [ShotBench](https://vchitect.github.io/ShotBench-project/), a comprehensive benchmark for evaluating cinematography understanding in vision-language models.
|
||||||
|
|
||||||
|
### Demo
|
||||||
|
|
||||||
|
**Image**
|
||||||
|
```python
|
||||||
|
import cv2
|
||||||
|
import torch
|
||||||
|
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
|
||||||
|
from qwen_vl_utils import process_vision_info
|
||||||
|
|
||||||
|
device = "cuda"
|
||||||
|
device_map = "balanced"
|
||||||
|
dtype = torch.bfloat16
|
||||||
|
image_path = "/path/to/image.jpg"
|
||||||
|
|
||||||
|
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||||
|
"Vchitect/ShotVL-3B",
|
||||||
|
device_map=device_map,
|
||||||
|
attn_implementation="flash_attention_2",
|
||||||
|
torch_dtype=dtype,
|
||||||
|
).eval()
|
||||||
|
processor = AutoProcessor.from_pretrained(
|
||||||
|
"Vchitect/ShotVL-3B", revision="refs/pr/24", use_fast=True, torch_dtype=dtype
|
||||||
|
)
|
||||||
|
|
||||||
|
SYSTEM_PROMPT = (
|
||||||
|
"A conversation between User and Assistant. The user asks a question, and the Assistant "
|
||||||
|
"solves it. The assistant first thinks about the reasoning process in the mind and then "
|
||||||
|
"provides the user with the answer. The reasoning process and answer are enclosed within "
|
||||||
|
"<think> </think> and <answer> </answer> tags."
|
||||||
|
)
|
||||||
|
|
||||||
|
msgs = [
|
||||||
|
{"role": "system", "content": SYSTEM_PROMPT},
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": [
|
||||||
|
{"type": "image", "image": image_path},
|
||||||
|
{"type": "text", "text": "What's the shot size of this shot?"},
|
||||||
|
],
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
text = processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
|
||||||
|
image_inputs, video_inputs = process_vision_info(msgs)
|
||||||
|
inputs = processor(
|
||||||
|
text=[text],
|
||||||
|
images=image_inputs,
|
||||||
|
videos=video_inputs,
|
||||||
|
padding=True,
|
||||||
|
return_tensors="pt",
|
||||||
|
).to(device)
|
||||||
|
|
||||||
|
with torch.inference_mode():
|
||||||
|
out_ids = model.generate(**inputs, max_new_tokens=640)
|
||||||
|
|
||||||
|
trimmed = [o[len(i):] for i, o in zip(inputs.input_ids, out_ids)]
|
||||||
|
print(processor.batch_decode(trimmed, skip_special_tokens=True)[0])
|
||||||
|
```
|
||||||
|
|
||||||
|
**Video**
|
||||||
|
|
||||||
|
```python
|
||||||
|
import cv2
|
||||||
|
import torch
|
||||||
|
from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
|
||||||
|
from qwen_vl_utils import process_vision_info
|
||||||
|
|
||||||
|
device = "cuda"
|
||||||
|
device_map = "balanced"
|
||||||
|
dtype = torch.bfloat16
|
||||||
|
video_path = "/path/to/video.mp4"
|
||||||
|
|
||||||
|
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||||
|
"Vchitect/ShotVL-3B",
|
||||||
|
device_map=device_map,
|
||||||
|
attn_implementation="flash_attention_2",
|
||||||
|
torch_dtype=dtype,
|
||||||
|
).eval()
|
||||||
|
processor = AutoProcessor.from_pretrained(
|
||||||
|
"Vchitect/ShotVL-3B", revision="refs/pr/24", use_fast=True, torch_dtype=dtype
|
||||||
|
)
|
||||||
|
|
||||||
|
question = (
|
||||||
|
"What's the camera movement in this movie shot?
|
||||||
|
"
|
||||||
|
"Options:
|
||||||
|
A. Boom down
|
||||||
|
B. Boom up
|
||||||
|
C. Push in
|
||||||
|
D. Pull out
|
||||||
|
"
|
||||||
|
"Please select the most likely answer from the options above.
|
||||||
|
"
|
||||||
|
)
|
||||||
|
|
||||||
|
msgs = [
|
||||||
|
{
|
||||||
|
"role": "user",
|
||||||
|
"content": [
|
||||||
|
{"type": "video", "video": video_path, "max_pixels": 360*640, "fps": 12.0},
|
||||||
|
{"type": "text", "text": question},
|
||||||
|
],
|
||||||
|
},
|
||||||
|
]
|
||||||
|
|
||||||
|
text = processor.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)
|
||||||
|
image_inputs, video_inputs = process_vision_info(msgs)
|
||||||
|
inputs = processor(
|
||||||
|
text=[text],
|
||||||
|
images=image_inputs,
|
||||||
|
videos=video_inputs,
|
||||||
|
padding=True,
|
||||||
|
return_tensors="pt",
|
||||||
|
).to(device)
|
||||||
|
|
||||||
|
with torch.inference_mode():
|
||||||
|
out_ids = model.generate(**inputs, max_new_tokens=640)
|
||||||
|
|
||||||
|
trimmed = [o[len(i):] for i, o in zip(inputs.input_ids, out_ids)]
|
||||||
|
print(processor.batch_decode(trimmed, skip_special_tokens=True)[0])
|
||||||
|
```
|
||||||
|
|
||||||
|
## Evaluation Results
|
||||||
|
|
||||||
|
<div align="center">
|
||||||
|
<table>
|
||||||
|
<caption>
|
||||||
|
<small>
|
||||||
|
Abbreviations:
|
||||||
|
SS = <em>Shot Size</em>,
|
||||||
|
SF = <em>Shot Framing</em>,
|
||||||
|
CA = <em>Camera Angle</em>,
|
||||||
|
LS = <em>Lens Size</em>,
|
||||||
|
LT = <em>Lighting Type</em>,
|
||||||
|
LC = <em>Lighting Conditions</em>,
|
||||||
|
SC = <em>Shot Composition</em>,
|
||||||
|
CM = <em>Camera Movement</em>.
|
||||||
|
<u>Underline</u> marks previous best in each group.<br>
|
||||||
|
<strong>Our <em>ShotVL</em> models establish new SOTA.</strong>
|
||||||
|
</small>
|
||||||
|
</caption><thead>
|
||||||
|
<tr>
|
||||||
|
<th>Models</th><th>SS</th><th>SF</th><th>CA</th><th>LS</th><th>LT</th>
|
||||||
|
<th>LC</th><th>SC</th><th>CM</th><th>Avg</th>
|
||||||
|
</tr>
|
||||||
|
</thead><tbody>
|
||||||
|
<tr><th colspan="10"><em>Open-Sourced VLMs</em></th></tr>
|
||||||
|
<tr><td>Qwen2.5-VL-3B-Instruct</td><td>54.6</td><td>56.6</td><td>43.1</td><td>36.6</td><td>59.3</td><td>45.1</td><td>41.5</td><td>31.9</td><td>46.1</td></tr>
|
||||||
|
<tr><td>Qwen2.5-VL-7B-Instruct</td><td>69.1</td><td>73.5</td><td>53.2</td><td>47.0</td><td>60.5</td><td>47.4</td><td>49.9</td><td>30.2</td><td>53.8</td></tr>
|
||||||
|
<tr><td>LLaVA-NeXT-Video-7B</td><td>35.9</td><td>37.1</td><td>32.5</td><td>27.8</td><td>50.9</td><td>31.7</td><td>28.0</td><td>31.3</td><td>34.4</td></tr>
|
||||||
|
<tr><td>LLaVA-Video-7B-Qwen2</td><td>56.9</td><td>65.4</td><td>45.1</td><td>36.0</td><td>63.5</td><td>45.4</td><td>37.4</td><td>35.3</td><td>48.1</td></tr>
|
||||||
|
<tr><td>LLaVA-Onevision-Qwen2-7B-Ov-Chat</td><td>58.4</td><td>71.0</td><td>52.3</td><td>38.7</td><td>59.5</td><td>44.9</td><td>50.9</td><td>39.7</td><td>51.9</td></tr>
|
||||||
|
<tr><td>InternVL2.5-8B</td><td>56.3</td><td>70.3</td><td>50.8</td><td>41.1</td><td>60.2</td><td>45.1</td><td>50.1</td><td>33.6</td><td>50.9</td></tr>
|
||||||
|
<tr><td>InternVL3-2B</td><td>56.3</td><td>56.0</td><td>44.4</td><td>34.6</td><td>56.8</td><td>44.6</td><td>43.0</td><td>38.1</td><td>46.7</td></tr>
|
||||||
|
<tr><td>InternVL3-8B</td><td>62.1</td><td>65.8</td><td>46.8</td><td>42.9</td><td>58.0</td><td>44.3</td><td>46.8</td><td>44.2</td><td>51.4</td></tr>
|
||||||
|
<tr><td>InternVL3-14B</td><td>59.6</td><td>82.2</td><td>55.4</td><td>40.7</td><td>61.7</td><td>44.6</td><td>51.1</td><td>38.2</td><td>54.2</td></tr>
|
||||||
|
<tr><td>Internlm-xcomposer2d5-7B</td><td>51.1</td><td>71.0</td><td>39.8</td><td>32.7</td><td>59.3</td><td>35.7</td><td>35.7</td><td>38.8</td><td>45.5</td></tr>
|
||||||
|
<tr><td>Ovis2-8B</td><td>35.9</td><td>37.1</td><td>32.5</td><td>27.8</td><td>50.9</td><td>31.7</td><td>28.0</td><td>35.3</td><td>34.9</td></tr>
|
||||||
|
<tr><td>VILA1.5-3B</td><td>33.4</td><td>44.9</td><td>32.1</td><td>28.6</td><td>50.6</td><td>35.7</td><td>28.4</td><td>21.5</td><td>34.4</td></tr>
|
||||||
|
<tr><td>VILA1.5-8B</td><td>40.6</td><td>44.5</td><td>39.1</td><td>29.7</td><td>48.9</td><td>32.9</td><td>34.4</td><td>36.9</td><td>38.4</td></tr>
|
||||||
|
<tr><td>VILA1.5-13B</td><td>36.7</td><td>54.6</td><td>40.7</td><td>34.8</td><td>52.8</td><td>35.4</td><td>34.2</td><td>31.3</td><td>40.1</td></tr>
|
||||||
|
<tr><td>Instructblip-vicuna-7B</td><td>27.0</td><td>27.9</td><td>34.5</td><td>29.4</td><td>44.4</td><td>29.7</td><td>27.1</td><td>25.0</td><td>30.6</td></tr>
|
||||||
|
<tr><td>Instructblip-vicuna-13B</td><td>26.8</td><td>29.2</td><td>27.9</td><td>28.0</td><td>39.0</td><td>24.0</td><td>27.1</td><td>22.0</td><td>28.0</td></tr>
|
||||||
|
<tr><td>InternVL2.5-38B</td><td>67.8</td><td><u>85.4</u></td><td>55.4</td><td>41.7</td><td>61.7</td><td>48.9</td><td>52.4</td><td>44.0</td><td>57.2</td></tr>
|
||||||
|
<tr><td>InternVL3-38B</td><td>68.0</td><td>84.0</td><td>51.9</td><td>43.6</td><td>64.4</td><td>46.9</td><td>54.7</td><td>44.6</td><td>57.3</td></tr>
|
||||||
|
<tr><td>Qwen2.5-VL-32B-Instruct</td><td>62.3</td><td>76.6</td><td>51.0</td><td>48.3</td><td>61.7</td><td>44.0</td><td>52.2</td><td>43.8</td><td>55.0</td></tr>
|
||||||
|
<tr><td>Qwen2.5-VL-72B-Instruct</td><td><u>75.1</u></td><td>82.9</td><td>56.7</td><td>46.8</td><td>59.0</td><td><u>49.4</u></td><td>54.1</td><td><u>48.9</u></td><td>59.1</td></tr>
|
||||||
|
<tr><td>InternVL3-78B</td><td>69.7</td><td>80.0</td><td>54.5</td><td>44.0</td><td><u>65.5</u></td><td>47.4</td><td>51.8</td><td>44.4</td><td>57.2</td></tr>
|
||||||
|
<tr><th colspan="10"><em>Proprietary VLMs</em></th></tr>
|
||||||
|
<tr><td>Gemini-2.0-flash</td><td>48.9</td><td>75.5</td><td>44.6</td><td>31.9</td><td>62.2</td><td>48.9</td><td>52.4</td><td>47.4</td><td>51.5</td></tr>
|
||||||
|
<tr><td>Gemini-2.5-flash-preview-04-17</td><td>57.7</td><td>82.9</td><td>51.4</td><td>43.8</td><td>65.2</td><td>45.7</td><td>45.9</td><td>43.5</td><td>54.5</td></tr>
|
||||||
|
<tr><td>GPT-4o</td><td>69.3</td><td>83.1</td><td><u>58.2</u></td><td><u>48.9</u></td><td>63.2</td><td>48.0</td><td><u>55.2</u></td><td>48.3</td><td><u>59.3</u></td></tr>
|
||||||
|
<tr><th colspan="10"><em>Ours</em></th></tr>
|
||||||
|
<tr>
|
||||||
|
<td>ShotVL-3B
|
||||||
|
<a href="https://huggingface.co/Vchitect/ShotVL-3B">
|
||||||
|
<img src="https://img.shields.io/badge/Model-HF-yellow?logo=huggingface" alt="HF">
|
||||||
|
</a>
|
||||||
|
</td>
|
||||||
|
<td>77.9</td><td>85.6</td><td>68.8</td><td>59.3</td><td>65.7</td>
|
||||||
|
<td>53.1</td><td>57.4</td><td>51.7</td><td>65.1</td>
|
||||||
|
</tr>
|
||||||
|
<tr>
|
||||||
|
<td>ShotVL-7B
|
||||||
|
<a href="https://huggingface.co/Vchitect/ShotVL-7B">
|
||||||
|
<img src="https://img.shields.io/badge/Model-HF-yellow?logo=huggingface" alt="HF">
|
||||||
|
</a>
|
||||||
|
</td>
|
||||||
|
<td>81.2</td><td>90.1</td><td>78.0</td><td>68.5</td><td>70.1</td>
|
||||||
|
<td>64.3</td><td>45.7</td><td>62.9</td><td>70.1</td>
|
||||||
|
</tr> </tbody>
|
||||||
|
</table></div>
|
||||||
|
|
||||||
|
## BibTeX
|
||||||
|
|
||||||
|
```
|
||||||
|
@misc{
|
||||||
|
liu2025shotbench,
|
||||||
|
title={ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models},
|
||||||
|
author={Hongbo Liu and Jingwen He and Yi Jin and Dian Zheng and Yuhao Dong and Fan Zhang and Ziqi Huang and Yinan He and Yangguang Li and Weichao Chen and Yu Qiao and Wanli Ouyang and Shengjie Zhao and Ziwei Liu},
|
||||||
|
year={2025},
|
||||||
|
eprint={2506.21356},
|
||||||
|
achivePrefix={arXiv},
|
||||||
|
primaryClass={cs.CV},
|
||||||
|
url={https://arxiv.org/abs/2506.21356},
|
||||||
|
}
|
||||||
|
```
|
||||||
24
added_tokens.json
Normal file
24
added_tokens.json
Normal file
@@ -0,0 +1,24 @@
|
|||||||
|
{
|
||||||
|
"</tool_call>": 151658,
|
||||||
|
"<tool_call>": 151657,
|
||||||
|
"<|box_end|>": 151649,
|
||||||
|
"<|box_start|>": 151648,
|
||||||
|
"<|endoftext|>": 151643,
|
||||||
|
"<|file_sep|>": 151664,
|
||||||
|
"<|fim_middle|>": 151660,
|
||||||
|
"<|fim_pad|>": 151662,
|
||||||
|
"<|fim_prefix|>": 151659,
|
||||||
|
"<|fim_suffix|>": 151661,
|
||||||
|
"<|im_end|>": 151645,
|
||||||
|
"<|im_start|>": 151644,
|
||||||
|
"<|image_pad|>": 151655,
|
||||||
|
"<|object_ref_end|>": 151647,
|
||||||
|
"<|object_ref_start|>": 151646,
|
||||||
|
"<|quad_end|>": 151651,
|
||||||
|
"<|quad_start|>": 151650,
|
||||||
|
"<|repo_name|>": 151663,
|
||||||
|
"<|video_pad|>": 151656,
|
||||||
|
"<|vision_end|>": 151653,
|
||||||
|
"<|vision_pad|>": 151654,
|
||||||
|
"<|vision_start|>": 151652
|
||||||
|
}
|
||||||
331
args.json
Normal file
331
args.json
Normal file
@@ -0,0 +1,331 @@
|
|||||||
|
{
|
||||||
|
"model": "/mnt/petrelfs/liuhongbo/Qwen2.5-VL-3B-Instruct",
|
||||||
|
"model_type": "qwen2_5_vl",
|
||||||
|
"model_revision": null,
|
||||||
|
"task_type": "causal_lm",
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"attn_impl": "flash_attn",
|
||||||
|
"num_labels": null,
|
||||||
|
"problem_type": null,
|
||||||
|
"rope_scaling": null,
|
||||||
|
"device_map": null,
|
||||||
|
"max_memory": {},
|
||||||
|
"local_repo_path": null,
|
||||||
|
"template": "qwen2_5_vl",
|
||||||
|
"system": "You are a helpful assistant.",
|
||||||
|
"max_length": 3072,
|
||||||
|
"truncation_strategy": "delete",
|
||||||
|
"max_pixels": null,
|
||||||
|
"agent_template": null,
|
||||||
|
"norm_bbox": null,
|
||||||
|
"response_prefix": null,
|
||||||
|
"padding_side": "right",
|
||||||
|
"loss_scale": "default",
|
||||||
|
"sequence_parallel_size": 1,
|
||||||
|
"use_chat_template": true,
|
||||||
|
"template_backend": "swift",
|
||||||
|
"dataset": [
|
||||||
|
"/mnt/petrelfs/liuhongbo/movie_bench/shotdeck_data/sft/img_train_v5_without_reasoning.json"
|
||||||
|
],
|
||||||
|
"val_dataset": [],
|
||||||
|
"split_dataset_ratio": 0.01,
|
||||||
|
"data_seed": 42,
|
||||||
|
"dataset_num_proc": 1,
|
||||||
|
"dataset_shuffle": true,
|
||||||
|
"val_dataset_shuffle": false,
|
||||||
|
"streaming": false,
|
||||||
|
"interleave_prob": null,
|
||||||
|
"stopping_strategy": "first_exhausted",
|
||||||
|
"shuffle_buffer_size": 1000,
|
||||||
|
"enable_cache": false,
|
||||||
|
"download_mode": "reuse_dataset_if_exists",
|
||||||
|
"columns": {},
|
||||||
|
"strict": false,
|
||||||
|
"remove_unused_columns": true,
|
||||||
|
"model_name": [
|
||||||
|
null,
|
||||||
|
null
|
||||||
|
],
|
||||||
|
"model_author": [
|
||||||
|
null,
|
||||||
|
null
|
||||||
|
],
|
||||||
|
"custom_dataset_info": [],
|
||||||
|
"quant_method": null,
|
||||||
|
"quant_bits": null,
|
||||||
|
"hqq_axis": null,
|
||||||
|
"bnb_4bit_compute_dtype": "bfloat16",
|
||||||
|
"bnb_4bit_quant_type": "nf4",
|
||||||
|
"bnb_4bit_use_double_quant": true,
|
||||||
|
"bnb_4bit_quant_storage": null,
|
||||||
|
"max_new_tokens": 64,
|
||||||
|
"temperature": 0.0,
|
||||||
|
"top_k": null,
|
||||||
|
"top_p": null,
|
||||||
|
"repetition_penalty": null,
|
||||||
|
"num_beams": 1,
|
||||||
|
"stream": false,
|
||||||
|
"stop_words": [],
|
||||||
|
"logprobs": false,
|
||||||
|
"top_logprobs": null,
|
||||||
|
"ckpt_dir": null,
|
||||||
|
"load_dataset_config": null,
|
||||||
|
"lora_modules": [],
|
||||||
|
"tuner_backend": "peft",
|
||||||
|
"train_type": "full",
|
||||||
|
"adapters": [],
|
||||||
|
"external_plugins": [],
|
||||||
|
"seed": 42,
|
||||||
|
"model_kwargs": {},
|
||||||
|
"load_args": false,
|
||||||
|
"load_data_args": false,
|
||||||
|
"use_hf": false,
|
||||||
|
"hub_token": null,
|
||||||
|
"custom_register_path": [],
|
||||||
|
"ignore_args_error": false,
|
||||||
|
"use_swift_lora": false,
|
||||||
|
"output_dir": "/mnt/petrelfs/liuhongbo/ms-swift/output/main/sft_without_reasoning/v0-20250505-134456",
|
||||||
|
"overwrite_output_dir": false,
|
||||||
|
"do_train": false,
|
||||||
|
"do_eval": false,
|
||||||
|
"do_predict": false,
|
||||||
|
"eval_strategy": "steps",
|
||||||
|
"prediction_loss_only": false,
|
||||||
|
"per_device_train_batch_size": 1,
|
||||||
|
"per_device_eval_batch_size": 1,
|
||||||
|
"per_gpu_train_batch_size": null,
|
||||||
|
"per_gpu_eval_batch_size": null,
|
||||||
|
"gradient_accumulation_steps": 16,
|
||||||
|
"eval_accumulation_steps": null,
|
||||||
|
"eval_delay": 0,
|
||||||
|
"torch_empty_cache_steps": null,
|
||||||
|
"learning_rate": 1e-05,
|
||||||
|
"weight_decay": 0.1,
|
||||||
|
"adam_beta1": 0.9,
|
||||||
|
"adam_beta2": 0.95,
|
||||||
|
"adam_epsilon": 1e-08,
|
||||||
|
"max_grad_norm": 1.0,
|
||||||
|
"num_train_epochs": 1.0,
|
||||||
|
"max_steps": -1,
|
||||||
|
"lr_scheduler_type": "cosine",
|
||||||
|
"lr_scheduler_kwargs": null,
|
||||||
|
"warmup_ratio": 0.05,
|
||||||
|
"warmup_steps": 0,
|
||||||
|
"log_level": "passive",
|
||||||
|
"log_level_replica": "warning",
|
||||||
|
"log_on_each_node": true,
|
||||||
|
"logging_dir": "/mnt/petrelfs/liuhongbo/ms-swift/output/main/sft_without_reasoning/v0-20250505-134456/runs",
|
||||||
|
"logging_strategy": "steps",
|
||||||
|
"logging_first_step": true,
|
||||||
|
"logging_steps": 5,
|
||||||
|
"logging_nan_inf_filter": true,
|
||||||
|
"save_strategy": "steps",
|
||||||
|
"save_steps": 100.0,
|
||||||
|
"save_total_limit": 3,
|
||||||
|
"save_safetensors": true,
|
||||||
|
"save_on_each_node": false,
|
||||||
|
"save_only_model": false,
|
||||||
|
"restore_callback_states_from_checkpoint": false,
|
||||||
|
"no_cuda": false,
|
||||||
|
"use_cpu": false,
|
||||||
|
"use_mps_device": false,
|
||||||
|
"jit_mode_eval": false,
|
||||||
|
"use_ipex": false,
|
||||||
|
"bf16": true,
|
||||||
|
"fp16": false,
|
||||||
|
"fp16_opt_level": "O1",
|
||||||
|
"half_precision_backend": "auto",
|
||||||
|
"bf16_full_eval": false,
|
||||||
|
"fp16_full_eval": false,
|
||||||
|
"tf32": null,
|
||||||
|
"local_rank": 0,
|
||||||
|
"ddp_backend": null,
|
||||||
|
"tpu_num_cores": null,
|
||||||
|
"tpu_metrics_debug": false,
|
||||||
|
"debug": null,
|
||||||
|
"dataloader_drop_last": false,
|
||||||
|
"eval_steps": 100.0,
|
||||||
|
"dataloader_num_workers": 4,
|
||||||
|
"dataloader_prefetch_factor": null,
|
||||||
|
"past_index": -1,
|
||||||
|
"run_name": null,
|
||||||
|
"disable_tqdm": null,
|
||||||
|
"label_names": null,
|
||||||
|
"load_best_model_at_end": false,
|
||||||
|
"metric_for_best_model": "loss",
|
||||||
|
"greater_is_better": false,
|
||||||
|
"ignore_data_skip": false,
|
||||||
|
"fsdp": "",
|
||||||
|
"fsdp_min_num_params": 0,
|
||||||
|
"fsdp_config": null,
|
||||||
|
"tp_size": 0,
|
||||||
|
"fsdp_transformer_layer_cls_to_wrap": null,
|
||||||
|
"accelerator_config": {
|
||||||
|
"dispatch_batches": false
|
||||||
|
},
|
||||||
|
"deepspeed": null,
|
||||||
|
"label_smoothing_factor": 0.0,
|
||||||
|
"optim": "adamw_torch",
|
||||||
|
"optim_args": null,
|
||||||
|
"adafactor": false,
|
||||||
|
"group_by_length": false,
|
||||||
|
"length_column_name": "length",
|
||||||
|
"report_to": [
|
||||||
|
"wandb"
|
||||||
|
],
|
||||||
|
"ddp_find_unused_parameters": null,
|
||||||
|
"ddp_bucket_cap_mb": null,
|
||||||
|
"ddp_broadcast_buffers": null,
|
||||||
|
"dataloader_pin_memory": true,
|
||||||
|
"dataloader_persistent_workers": false,
|
||||||
|
"skip_memory_metrics": true,
|
||||||
|
"use_legacy_prediction_loop": false,
|
||||||
|
"push_to_hub": false,
|
||||||
|
"resume_from_checkpoint": null,
|
||||||
|
"hub_model_id": null,
|
||||||
|
"hub_strategy": "every_save",
|
||||||
|
"hub_private_repo": null,
|
||||||
|
"hub_always_push": false,
|
||||||
|
"gradient_checkpointing": true,
|
||||||
|
"gradient_checkpointing_kwargs": null,
|
||||||
|
"include_inputs_for_metrics": false,
|
||||||
|
"include_for_metrics": [],
|
||||||
|
"eval_do_concat_batches": true,
|
||||||
|
"fp16_backend": "auto",
|
||||||
|
"evaluation_strategy": "steps",
|
||||||
|
"push_to_hub_model_id": null,
|
||||||
|
"push_to_hub_organization": null,
|
||||||
|
"push_to_hub_token": null,
|
||||||
|
"mp_parameters": "",
|
||||||
|
"auto_find_batch_size": false,
|
||||||
|
"full_determinism": false,
|
||||||
|
"torchdynamo": null,
|
||||||
|
"ray_scope": "last",
|
||||||
|
"ddp_timeout": 1800,
|
||||||
|
"torch_compile": false,
|
||||||
|
"torch_compile_backend": null,
|
||||||
|
"torch_compile_mode": null,
|
||||||
|
"dispatch_batches": null,
|
||||||
|
"split_batches": null,
|
||||||
|
"include_tokens_per_second": false,
|
||||||
|
"include_num_input_tokens_seen": false,
|
||||||
|
"neftune_noise_alpha": null,
|
||||||
|
"optim_target_modules": null,
|
||||||
|
"batch_eval_metrics": false,
|
||||||
|
"eval_on_start": false,
|
||||||
|
"use_liger_kernel": false,
|
||||||
|
"eval_use_gather_object": false,
|
||||||
|
"average_tokens_across_devices": false,
|
||||||
|
"sortish_sampler": false,
|
||||||
|
"predict_with_generate": false,
|
||||||
|
"generation_max_length": null,
|
||||||
|
"generation_num_beams": null,
|
||||||
|
"generation_config": null,
|
||||||
|
"check_model": true,
|
||||||
|
"acc_strategy": "token",
|
||||||
|
"train_dataloader_shuffle": true,
|
||||||
|
"metric_warmup_step": 0,
|
||||||
|
"fsdp_num": 1,
|
||||||
|
"acc_steps": 1,
|
||||||
|
"eval_use_evalscope": false,
|
||||||
|
"eval_datasets": [],
|
||||||
|
"eval_limit": null,
|
||||||
|
"eval_datasets_args": null,
|
||||||
|
"eval_generation_config": null,
|
||||||
|
"freeze_parameters": [
|
||||||
|
"visual",
|
||||||
|
"visual.merger"
|
||||||
|
],
|
||||||
|
"freeze_parameters_ratio": 0.0,
|
||||||
|
"trainable_parameters": [],
|
||||||
|
"freeze_llm": false,
|
||||||
|
"freeze_vit": true,
|
||||||
|
"freeze_aligner": true,
|
||||||
|
"target_modules": [
|
||||||
|
"all-linear"
|
||||||
|
],
|
||||||
|
"target_regex": null,
|
||||||
|
"modules_to_save": [],
|
||||||
|
"lora_rank": 8,
|
||||||
|
"lora_alpha": 32,
|
||||||
|
"lora_dropout": 0.05,
|
||||||
|
"lora_bias": "none",
|
||||||
|
"lora_dtype": null,
|
||||||
|
"lorap_lr_ratio": null,
|
||||||
|
"use_rslora": false,
|
||||||
|
"use_dora": false,
|
||||||
|
"lora_ga_batch_size": 2,
|
||||||
|
"lora_ga_iters": 2,
|
||||||
|
"lora_ga_max_length": 1024,
|
||||||
|
"lora_ga_direction": "ArB2r",
|
||||||
|
"lora_ga_scale": "stable",
|
||||||
|
"lora_ga_stable_gamma": 16,
|
||||||
|
"init_weights": true,
|
||||||
|
"fourier_n_frequency": 2000,
|
||||||
|
"fourier_scaling": 300.0,
|
||||||
|
"boft_block_size": 4,
|
||||||
|
"boft_block_num": 0,
|
||||||
|
"boft_n_butterfly_factor": 1,
|
||||||
|
"boft_dropout": 0.0,
|
||||||
|
"vera_rank": 256,
|
||||||
|
"vera_projection_prng_key": 0,
|
||||||
|
"vera_dropout": 0.0,
|
||||||
|
"vera_d_initial": 0.1,
|
||||||
|
"adapter_act": "gelu",
|
||||||
|
"adapter_length": 128,
|
||||||
|
"use_galore": false,
|
||||||
|
"galore_target_modules": null,
|
||||||
|
"galore_rank": 128,
|
||||||
|
"galore_update_proj_gap": 50,
|
||||||
|
"galore_scale": 1.0,
|
||||||
|
"galore_proj_type": "std",
|
||||||
|
"galore_optim_per_parameter": false,
|
||||||
|
"galore_with_embedding": false,
|
||||||
|
"galore_quantization": false,
|
||||||
|
"galore_proj_quant": false,
|
||||||
|
"galore_proj_bits": 4,
|
||||||
|
"galore_proj_group_size": 256,
|
||||||
|
"galore_cos_threshold": 0.4,
|
||||||
|
"galore_gamma_proj": 2,
|
||||||
|
"galore_queue_size": 5,
|
||||||
|
"adalora_target_r": 8,
|
||||||
|
"adalora_init_r": 12,
|
||||||
|
"adalora_tinit": 0,
|
||||||
|
"adalora_tfinal": 0,
|
||||||
|
"adalora_deltaT": 1,
|
||||||
|
"adalora_beta1": 0.85,
|
||||||
|
"adalora_beta2": 0.85,
|
||||||
|
"adalora_orth_reg_weight": 0.5,
|
||||||
|
"llamapro_num_new_blocks": 4,
|
||||||
|
"llamapro_num_groups": null,
|
||||||
|
"lisa_activated_layers": 0,
|
||||||
|
"lisa_step_interval": 20,
|
||||||
|
"reft_layer_key": null,
|
||||||
|
"reft_layers": null,
|
||||||
|
"reft_rank": 4,
|
||||||
|
"reft_intervention_type": "LoreftIntervention",
|
||||||
|
"reft_args": null,
|
||||||
|
"swanlab_token": null,
|
||||||
|
"swanlab_project": null,
|
||||||
|
"swanlab_workspace": null,
|
||||||
|
"swanlab_exp_name": null,
|
||||||
|
"swanlab_mode": "cloud",
|
||||||
|
"add_version": true,
|
||||||
|
"resume_only_model": false,
|
||||||
|
"create_checkpoint_symlink": false,
|
||||||
|
"packing": false,
|
||||||
|
"lazy_tokenize": true,
|
||||||
|
"loss_type": null,
|
||||||
|
"optimizer": null,
|
||||||
|
"metric": null,
|
||||||
|
"zero_hpz_partition_size": null,
|
||||||
|
"rank": 0,
|
||||||
|
"global_world_size": 4,
|
||||||
|
"local_world_size": 4,
|
||||||
|
"model_suffix": "Qwen2.5-VL-3B-Instruct",
|
||||||
|
"model_info": "ModelInfo(model_type='qwen2_5_vl', model_dir='/mnt/petrelfs/liuhongbo/Qwen2.5-VL-3B-Instruct', torch_dtype=torch.bfloat16, max_model_len=128000, quant_method=None, quant_bits=None, rope_scaling={'type': 'default', 'mrope_section': [16, 24, 24], 'rope_type': 'default'}, config=None, task_type='causal_lm', num_labels=None)",
|
||||||
|
"model_meta": "ModelMeta(model_type='qwen2_5_vl', model_groups=[ModelGroup(models=[Model(ms_model_id='Qwen/Qwen2.5-VL-3B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-3B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-7B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-7B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-32B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-32B-Instruct', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-72B-Instruct', hf_model_id='Qwen/Qwen2.5-VL-72B-Instruct', model_path=None, ms_revision=None, hf_revision=None)], ignore_patterns=None, requires=None, tags=[]), ModelGroup(models=[Model(ms_model_id='Qwen/Qwen2.5-VL-3B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-3B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-7B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-7B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-32B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-32B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None), Model(ms_model_id='Qwen/Qwen2.5-VL-72B-Instruct-AWQ', hf_model_id='Qwen/Qwen2.5-VL-72B-Instruct-AWQ', model_path=None, ms_revision=None, hf_revision=None)], ignore_patterns=None, requires=None, tags=[])], template='qwen2_5_vl', get_function=<function get_model_tokenizer_qwen2_5_vl at 0x7fd8f7224670>, model_arch='qwen2_vl', architectures=['Qwen2_5_VLForConditionalGeneration'], additional_saved_files=[], torch_dtype=None, is_multimodal=True, is_reward=False, task_type=None, ignore_patterns=None, requires=['transformers>=4.49', 'qwen_vl_utils>=0.0.6', 'decord'], tags=[])",
|
||||||
|
"model_dir": "/mnt/petrelfs/liuhongbo/Qwen2.5-VL-3B-Instruct",
|
||||||
|
"hub": "<class 'swift.hub.hub.MSHub'>",
|
||||||
|
"training_args": "Seq2SeqTrainingArguments(output_dir='/mnt/petrelfs/liuhongbo/ms-swift/output/main/sft_without_reasoning/v0-20250505-134456', overwrite_output_dir=False, do_train=False, do_eval=True, do_predict=False, eval_strategy=<IntervalStrategy.STEPS: 'steps'>, prediction_loss_only=False, per_device_train_batch_size=1, per_device_eval_batch_size=1, per_gpu_train_batch_size=None, per_gpu_eval_batch_size=None, gradient_accumulation_steps=16, eval_accumulation_steps=None, eval_delay=0, torch_empty_cache_steps=None, learning_rate=1e-05, weight_decay=0.1, adam_beta1=0.9, adam_beta2=0.95, adam_epsilon=1e-08, max_grad_norm=1.0, num_train_epochs=1.0, max_steps=-1, lr_scheduler_type=<SchedulerType.COSINE: 'cosine'>, lr_scheduler_kwargs=None, warmup_ratio=0.05, warmup_steps=0, log_level='passive', log_level_replica='warning', log_on_each_node=True, logging_dir='/mnt/petrelfs/liuhongbo/ms-swift/output/main/sft_without_reasoning/v0-20250505-134456/runs', logging_strategy=<IntervalStrategy.STEPS: 'steps'>, logging_first_step=True, logging_steps=5, logging_nan_inf_filter=True, save_strategy=<SaveStrategy.STEPS: 'steps'>, save_steps=100, save_total_limit=3, save_safetensors=True, save_on_each_node=False, save_only_model=False, restore_callback_states_from_checkpoint=False, no_cuda=False, use_cpu=False, use_mps_device=False, seed=42, data_seed=42, jit_mode_eval=False, use_ipex=False, bf16=True, fp16=False, fp16_opt_level='O1', half_precision_backend='auto', bf16_full_eval=False, fp16_full_eval=False, tf32=None, local_rank=0, ddp_backend=None, tpu_num_cores=None, tpu_metrics_debug=False, debug=[], dataloader_drop_last=False, eval_steps=100, dataloader_num_workers=4, dataloader_prefetch_factor=10, past_index=-1, run_name='/mnt/petrelfs/liuhongbo/ms-swift/output/main/sft_without_reasoning/v0-20250505-134456', disable_tqdm=False, remove_unused_columns=False, label_names=None, load_best_model_at_end=False, metric_for_best_model='loss', greater_is_better=False, ignore_data_skip=False, fsdp=[], fsdp_min_num_params=0, fsdp_config={'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}, tp_size=0, fsdp_transformer_layer_cls_to_wrap=None, accelerator_config=AcceleratorConfig(split_batches=False, dispatch_batches=False, even_batches=True, use_seedable_sampler=True, non_blocking=False, gradient_accumulation_kwargs=None, use_configured_state=False), deepspeed=None, label_smoothing_factor=0.0, optim=<OptimizerNames.ADAMW_TORCH: 'adamw_torch'>, optim_args=None, adafactor=False, group_by_length=False, length_column_name='length', report_to=['wandb'], ddp_find_unused_parameters=None, ddp_bucket_cap_mb=None, ddp_broadcast_buffers=None, dataloader_pin_memory=True, dataloader_persistent_workers=False, skip_memory_metrics=True, use_legacy_prediction_loop=False, push_to_hub=False, resume_from_checkpoint=None, hub_model_id=None, hub_strategy=<HubStrategy.EVERY_SAVE: 'every_save'>, hub_token=None, hub_private_repo=None, hub_always_push=False, gradient_checkpointing=True, gradient_checkpointing_kwargs=None, include_inputs_for_metrics=False, include_for_metrics=[], eval_do_concat_batches=True, fp16_backend='auto', evaluation_strategy='steps', push_to_hub_model_id=None, push_to_hub_organization=None, push_to_hub_token=None, mp_parameters='', auto_find_batch_size=False, full_determinism=False, torchdynamo=None, ray_scope='last', ddp_timeout=1800, torch_compile=False, torch_compile_backend=None, torch_compile_mode=None, dispatch_batches=None, split_batches=None, include_tokens_per_second=None, include_num_input_tokens_seen=None, neftune_noise_alpha=None, optim_target_modules=None, batch_eval_metrics=False, eval_on_start=False, use_liger_kernel=False, eval_use_gather_object=False, average_tokens_across_devices=None, sortish_sampler=False, predict_with_generate=False, generation_max_length=None, generation_num_beams=None, generation_config=None, check_model=True, acc_strategy='token', train_dataloader_shuffle=True, metric_warmup_step=0, fsdp_num=1, acc_steps=1, eval_use_evalscope=False, eval_datasets=[], eval_limit=None, eval_datasets_args=None, eval_generation_config=None, train_type='full', optimizer=None, local_repo_path=None, galore_config=None)"
|
||||||
|
}
|
||||||
3
chat_template.json
Normal file
3
chat_template.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
{
|
||||||
|
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
|
||||||
|
}
|
||||||
66
config.json
Normal file
66
config.json
Normal file
@@ -0,0 +1,66 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2_5_VLForConditionalGeneration"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 2048,
|
||||||
|
"image_token_id": 151655,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 11008,
|
||||||
|
"max_position_embeddings": 128000,
|
||||||
|
"max_window_layers": 70,
|
||||||
|
"model_type": "qwen2_5_vl",
|
||||||
|
"num_attention_heads": 16,
|
||||||
|
"num_hidden_layers": 36,
|
||||||
|
"num_key_value_heads": 2,
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_scaling": {
|
||||||
|
"mrope_section": [
|
||||||
|
16,
|
||||||
|
24,
|
||||||
|
24
|
||||||
|
],
|
||||||
|
"rope_type": "default",
|
||||||
|
"type": "default"
|
||||||
|
},
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"sliding_window": 32768,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"transformers_version": "4.50.3",
|
||||||
|
"use_cache": false,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"video_token_id": 151656,
|
||||||
|
"vision_config": {
|
||||||
|
"depth": 32,
|
||||||
|
"fullatt_block_indexes": [
|
||||||
|
7,
|
||||||
|
15,
|
||||||
|
23,
|
||||||
|
31
|
||||||
|
],
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 1280,
|
||||||
|
"in_channels": 3,
|
||||||
|
"in_chans": 3,
|
||||||
|
"intermediate_size": 3420,
|
||||||
|
"model_type": "qwen2_5_vl",
|
||||||
|
"num_heads": 16,
|
||||||
|
"out_hidden_size": 2048,
|
||||||
|
"patch_size": 14,
|
||||||
|
"spatial_merge_size": 2,
|
||||||
|
"spatial_patch_size": 14,
|
||||||
|
"temporal_patch_size": 2,
|
||||||
|
"tokens_per_second": 2,
|
||||||
|
"torch_dtype": "bfloat16",
|
||||||
|
"window_size": 112
|
||||||
|
},
|
||||||
|
"vision_end_token_id": 151653,
|
||||||
|
"vision_start_token_id": 151652,
|
||||||
|
"vision_token_id": 151654,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
12
generation_config.json
Normal file
12
generation_config.json
Normal file
@@ -0,0 +1,12 @@
|
|||||||
|
{
|
||||||
|
"bos_token_id": 151643,
|
||||||
|
"do_sample": true,
|
||||||
|
"eos_token_id": [
|
||||||
|
151645,
|
||||||
|
151643
|
||||||
|
],
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"repetition_penalty": 1.05,
|
||||||
|
"temperature": 1e-06,
|
||||||
|
"transformers_version": "4.50.3"
|
||||||
|
}
|
||||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:eb59dc1dc0b59c9c40b7c20aab0a0776c46987ae65cc05fd29c9f05caeaaa3b2
|
||||||
|
size 4997750760
|
||||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:ab6089fe225b954a227ec2bd7fee565b4d5140740eee41324cce231b0534ec6f
|
||||||
|
size 2511587184
|
||||||
831
model.safetensors.index.json
Normal file
831
model.safetensors.index.json
Normal file
@@ -0,0 +1,831 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
||||||
|
"total_size": 7509245952
|
||||||
|
},
|
||||||
|
"weight_map": {
|
||||||
|
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.10.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.11.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.12.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.13.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.14.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.15.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.16.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.17.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.18.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.19.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.19.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.19.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.20.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.20.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.21.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.22.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.23.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.24.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.25.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.26.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.27.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.28.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.29.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.3.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.30.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.30.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.31.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.32.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.33.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.34.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.input_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.mlp.down_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.mlp.gate_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.mlp.up_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.post_attention_layernorm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.k_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.k_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.o_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.q_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.q_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.v_proj.bias": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.35.self_attn.v_proj.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"model.layers.4.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.input_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.post_attention_layernorm.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.o_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"model.norm.weight": "model-00002-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.0.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.1.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.10.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.11.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.12.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.13.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.14.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.15.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.16.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.17.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.18.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.19.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.2.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.20.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.21.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.22.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.23.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.24.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.25.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.26.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.27.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.28.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.29.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.3.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.30.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.31.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.4.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.5.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.6.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.7.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.8.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.attn.proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.attn.proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.attn.qkv.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.attn.qkv.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.down_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.down_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.gate_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.gate_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.up_proj.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.mlp.up_proj.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.norm1.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.blocks.9.norm2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.merger.ln_q.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.merger.mlp.0.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.merger.mlp.0.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.merger.mlp.2.bias": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.merger.mlp.2.weight": "model-00001-of-00002.safetensors",
|
||||||
|
"visual.patch_embed.proj.weight": "model-00001-of-00002.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
19
preprocessor_config.json
Normal file
19
preprocessor_config.json
Normal file
@@ -0,0 +1,19 @@
|
|||||||
|
{
|
||||||
|
"min_pixels": 3136,
|
||||||
|
"max_pixels": 12845056,
|
||||||
|
"patch_size": 14,
|
||||||
|
"temporal_patch_size": 2,
|
||||||
|
"merge_size": 2,
|
||||||
|
"image_mean": [
|
||||||
|
0.48145466,
|
||||||
|
0.4578275,
|
||||||
|
0.40821073
|
||||||
|
],
|
||||||
|
"image_std": [
|
||||||
|
0.26862954,
|
||||||
|
0.26130258,
|
||||||
|
0.27577711
|
||||||
|
],
|
||||||
|
"image_processor_type": "Qwen2VLImageProcessor",
|
||||||
|
"processor_class": "Qwen2_5_VLProcessor"
|
||||||
|
}
|
||||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||||
|
size 11421896
|
||||||
209
tokenizer_config.json
Normal file
209
tokenizer_config.json
Normal file
@@ -0,0 +1,209 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": false,
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>",
|
||||||
|
"<|object_ref_start|>",
|
||||||
|
"<|object_ref_end|>",
|
||||||
|
"<|box_start|>",
|
||||||
|
"<|box_end|>",
|
||||||
|
"<|quad_start|>",
|
||||||
|
"<|quad_end|>",
|
||||||
|
"<|vision_start|>",
|
||||||
|
"<|vision_end|>",
|
||||||
|
"<|vision_pad|>",
|
||||||
|
"<|image_pad|>",
|
||||||
|
"<|video_pad|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- '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 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",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"extra_special_tokens": {},
|
||||||
|
"model_max_length": 131072,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"processor_class": "Qwen2_5_VLProcessor",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
140944
trainer_state.json
Normal file
140944
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:29d9c1908e4acaf41cb93e7a17780b33063c66d384fd432f19a72809463639c7
|
||||||
|
size 9976
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
604
zero_to_fp32.py
Normal file
604
zero_to_fp32.py
Normal file
@@ -0,0 +1,604 @@
|
|||||||
|
#!/usr/bin/env python
|
||||||
|
|
||||||
|
# Copyright (c) Microsoft Corporation.
|
||||||
|
# SPDX-License-Identifier: Apache-2.0
|
||||||
|
|
||||||
|
# DeepSpeed Team
|
||||||
|
|
||||||
|
# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
|
||||||
|
# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
|
||||||
|
# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
|
||||||
|
# application.
|
||||||
|
#
|
||||||
|
# example: python zero_to_fp32.py . pytorch_model.bin
|
||||||
|
|
||||||
|
import argparse
|
||||||
|
import torch
|
||||||
|
import glob
|
||||||
|
import math
|
||||||
|
import os
|
||||||
|
import re
|
||||||
|
from collections import OrderedDict
|
||||||
|
from dataclasses import dataclass
|
||||||
|
|
||||||
|
# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
|
||||||
|
# DeepSpeed data structures it has to be available in the current python environment.
|
||||||
|
from deepspeed.utils import logger
|
||||||
|
from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
|
||||||
|
FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
|
||||||
|
FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
|
||||||
|
|
||||||
|
|
||||||
|
@dataclass
|
||||||
|
class zero_model_state:
|
||||||
|
buffers: dict()
|
||||||
|
param_shapes: dict()
|
||||||
|
shared_params: list
|
||||||
|
ds_version: int
|
||||||
|
frozen_param_shapes: dict()
|
||||||
|
frozen_param_fragments: dict()
|
||||||
|
|
||||||
|
|
||||||
|
debug = 0
|
||||||
|
|
||||||
|
# load to cpu
|
||||||
|
device = torch.device('cpu')
|
||||||
|
|
||||||
|
|
||||||
|
def atoi(text):
|
||||||
|
return int(text) if text.isdigit() else text
|
||||||
|
|
||||||
|
|
||||||
|
def natural_keys(text):
|
||||||
|
'''
|
||||||
|
alist.sort(key=natural_keys) sorts in human order
|
||||||
|
http://nedbatchelder.com/blog/200712/human_sorting.html
|
||||||
|
(See Toothy's implementation in the comments)
|
||||||
|
'''
|
||||||
|
return [atoi(c) for c in re.split(r'(\d+)', text)]
|
||||||
|
|
||||||
|
|
||||||
|
def get_model_state_file(checkpoint_dir, zero_stage):
|
||||||
|
if not os.path.isdir(checkpoint_dir):
|
||||||
|
raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
|
||||||
|
|
||||||
|
# there should be only one file
|
||||||
|
if zero_stage <= 2:
|
||||||
|
file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
|
||||||
|
elif zero_stage == 3:
|
||||||
|
file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
|
||||||
|
|
||||||
|
if not os.path.exists(file):
|
||||||
|
raise FileNotFoundError(f"can't find model states file at '{file}'")
|
||||||
|
|
||||||
|
return file
|
||||||
|
|
||||||
|
|
||||||
|
def get_checkpoint_files(checkpoint_dir, glob_pattern):
|
||||||
|
# XXX: need to test that this simple glob rule works for multi-node setup too
|
||||||
|
ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
|
||||||
|
|
||||||
|
if len(ckpt_files) == 0:
|
||||||
|
raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
|
||||||
|
|
||||||
|
return ckpt_files
|
||||||
|
|
||||||
|
|
||||||
|
def get_optim_files(checkpoint_dir):
|
||||||
|
return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
|
||||||
|
|
||||||
|
|
||||||
|
def get_model_state_files(checkpoint_dir):
|
||||||
|
return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
|
||||||
|
|
||||||
|
|
||||||
|
def parse_model_states(files):
|
||||||
|
zero_model_states = []
|
||||||
|
for file in files:
|
||||||
|
state_dict = torch.load(file, map_location=device)
|
||||||
|
|
||||||
|
if BUFFER_NAMES not in state_dict:
|
||||||
|
raise ValueError(f"{file} is not a model state checkpoint")
|
||||||
|
buffer_names = state_dict[BUFFER_NAMES]
|
||||||
|
if debug:
|
||||||
|
print("Found buffers:", buffer_names)
|
||||||
|
|
||||||
|
# recover just the buffers while restoring them to fp32 if they were saved in fp16
|
||||||
|
buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
|
||||||
|
param_shapes = state_dict[PARAM_SHAPES]
|
||||||
|
|
||||||
|
# collect parameters that are included in param_shapes
|
||||||
|
param_names = []
|
||||||
|
for s in param_shapes:
|
||||||
|
for name in s.keys():
|
||||||
|
param_names.append(name)
|
||||||
|
|
||||||
|
# update with frozen parameters
|
||||||
|
frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
|
||||||
|
if frozen_param_shapes is not None:
|
||||||
|
if debug:
|
||||||
|
print(f"Found frozen_param_shapes: {frozen_param_shapes}")
|
||||||
|
param_names += list(frozen_param_shapes.keys())
|
||||||
|
|
||||||
|
# handle shared params
|
||||||
|
shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
|
||||||
|
|
||||||
|
ds_version = state_dict.get(DS_VERSION, None)
|
||||||
|
|
||||||
|
frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
|
||||||
|
|
||||||
|
z_model_state = zero_model_state(buffers=buffers,
|
||||||
|
param_shapes=param_shapes,
|
||||||
|
shared_params=shared_params,
|
||||||
|
ds_version=ds_version,
|
||||||
|
frozen_param_shapes=frozen_param_shapes,
|
||||||
|
frozen_param_fragments=frozen_param_fragments)
|
||||||
|
zero_model_states.append(z_model_state)
|
||||||
|
|
||||||
|
return zero_model_states
|
||||||
|
|
||||||
|
|
||||||
|
def parse_optim_states(files, ds_checkpoint_dir):
|
||||||
|
|
||||||
|
total_files = len(files)
|
||||||
|
state_dicts = []
|
||||||
|
for f in files:
|
||||||
|
state_dict = torch.load(f, map_location=device)
|
||||||
|
# immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
|
||||||
|
# and also handle the case where it was already removed by another helper script
|
||||||
|
state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
|
||||||
|
state_dicts.append(state_dict)
|
||||||
|
|
||||||
|
if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
|
||||||
|
raise ValueError(f"{files[0]} is not a zero checkpoint")
|
||||||
|
zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
|
||||||
|
world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
|
||||||
|
|
||||||
|
# For ZeRO-2 each param group can have different partition_count as data parallelism for expert
|
||||||
|
# parameters can be different from data parallelism for non-expert parameters. So we can just
|
||||||
|
# use the max of the partition_count to get the dp world_size.
|
||||||
|
|
||||||
|
if type(world_size) is list:
|
||||||
|
world_size = max(world_size)
|
||||||
|
|
||||||
|
if world_size != total_files:
|
||||||
|
raise ValueError(
|
||||||
|
f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
|
||||||
|
"Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
|
||||||
|
)
|
||||||
|
|
||||||
|
# the groups are named differently in each stage
|
||||||
|
if zero_stage <= 2:
|
||||||
|
fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
|
||||||
|
elif zero_stage == 3:
|
||||||
|
fp32_groups_key = FP32_FLAT_GROUPS
|
||||||
|
else:
|
||||||
|
raise ValueError(f"unknown zero stage {zero_stage}")
|
||||||
|
|
||||||
|
if zero_stage <= 2:
|
||||||
|
fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
|
||||||
|
elif zero_stage == 3:
|
||||||
|
# if there is more than one param group, there will be multiple flattened tensors - one
|
||||||
|
# flattened tensor per group - for simplicity merge them into a single tensor
|
||||||
|
#
|
||||||
|
# XXX: could make the script more memory efficient for when there are multiple groups - it
|
||||||
|
# will require matching the sub-lists of param_shapes for each param group flattened tensor
|
||||||
|
|
||||||
|
fp32_flat_groups = [
|
||||||
|
torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
|
||||||
|
]
|
||||||
|
|
||||||
|
return zero_stage, world_size, fp32_flat_groups
|
||||||
|
|
||||||
|
|
||||||
|
def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
|
||||||
|
"""
|
||||||
|
Returns fp32 state_dict reconstructed from ds checkpoint
|
||||||
|
|
||||||
|
Args:
|
||||||
|
- ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
|
||||||
|
|
||||||
|
"""
|
||||||
|
print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
|
||||||
|
|
||||||
|
optim_files = get_optim_files(ds_checkpoint_dir)
|
||||||
|
zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
|
||||||
|
print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
|
||||||
|
|
||||||
|
model_files = get_model_state_files(ds_checkpoint_dir)
|
||||||
|
|
||||||
|
zero_model_states = parse_model_states(model_files)
|
||||||
|
print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
|
||||||
|
|
||||||
|
if zero_stage <= 2:
|
||||||
|
return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||||
|
exclude_frozen_parameters)
|
||||||
|
elif zero_stage == 3:
|
||||||
|
return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||||
|
exclude_frozen_parameters)
|
||||||
|
|
||||||
|
|
||||||
|
def _zero2_merge_frozen_params(state_dict, zero_model_states):
|
||||||
|
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
||||||
|
return
|
||||||
|
|
||||||
|
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
||||||
|
frozen_param_fragments = zero_model_states[0].frozen_param_fragments
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
num_elem = sum(s.numel() for s in frozen_param_shapes.values())
|
||||||
|
print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
||||||
|
|
||||||
|
wanted_params = len(frozen_param_shapes)
|
||||||
|
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
||||||
|
avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
|
||||||
|
print(f'Frozen params: Have {avail_numel} numels to process.')
|
||||||
|
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
||||||
|
|
||||||
|
total_params = 0
|
||||||
|
total_numel = 0
|
||||||
|
for name, shape in frozen_param_shapes.items():
|
||||||
|
total_params += 1
|
||||||
|
unpartitioned_numel = shape.numel()
|
||||||
|
total_numel += unpartitioned_numel
|
||||||
|
|
||||||
|
state_dict[name] = frozen_param_fragments[name]
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
||||||
|
|
||||||
|
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
||||||
|
|
||||||
|
|
||||||
|
def _has_callable(obj, fn):
|
||||||
|
attr = getattr(obj, fn, None)
|
||||||
|
return callable(attr)
|
||||||
|
|
||||||
|
|
||||||
|
def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
||||||
|
param_shapes = zero_model_states[0].param_shapes
|
||||||
|
|
||||||
|
# Reconstruction protocol:
|
||||||
|
#
|
||||||
|
# XXX: document this
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
for i in range(world_size):
|
||||||
|
for j in range(len(fp32_flat_groups[0])):
|
||||||
|
print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
|
||||||
|
|
||||||
|
# XXX: memory usage doubles here (zero2)
|
||||||
|
num_param_groups = len(fp32_flat_groups[0])
|
||||||
|
merged_single_partition_of_fp32_groups = []
|
||||||
|
for i in range(num_param_groups):
|
||||||
|
merged_partitions = [sd[i] for sd in fp32_flat_groups]
|
||||||
|
full_single_fp32_vector = torch.cat(merged_partitions, 0)
|
||||||
|
merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
|
||||||
|
avail_numel = sum(
|
||||||
|
[full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
wanted_params = sum([len(shapes) for shapes in param_shapes])
|
||||||
|
wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
|
||||||
|
# not asserting if there is a mismatch due to possible padding
|
||||||
|
print(f"Have {avail_numel} numels to process.")
|
||||||
|
print(f"Need {wanted_numel} numels in {wanted_params} params.")
|
||||||
|
|
||||||
|
# params
|
||||||
|
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
||||||
|
# out-of-core computing solution
|
||||||
|
total_numel = 0
|
||||||
|
total_params = 0
|
||||||
|
for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
|
||||||
|
offset = 0
|
||||||
|
avail_numel = full_single_fp32_vector.numel()
|
||||||
|
for name, shape in shapes.items():
|
||||||
|
|
||||||
|
unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
|
||||||
|
total_numel += unpartitioned_numel
|
||||||
|
total_params += 1
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
|
||||||
|
state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
|
||||||
|
offset += unpartitioned_numel
|
||||||
|
|
||||||
|
# Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
|
||||||
|
# avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
|
||||||
|
# paddings performed in the code it's almost impossible to predict the exact numbers w/o the
|
||||||
|
# live optimizer object, so we are checking that the numbers are within the right range
|
||||||
|
align_to = 2 * world_size
|
||||||
|
|
||||||
|
def zero2_align(x):
|
||||||
|
return align_to * math.ceil(x / align_to)
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(f"original offset={offset}, avail_numel={avail_numel}")
|
||||||
|
|
||||||
|
offset = zero2_align(offset)
|
||||||
|
avail_numel = zero2_align(avail_numel)
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(f"aligned offset={offset}, avail_numel={avail_numel}")
|
||||||
|
|
||||||
|
# Sanity check
|
||||||
|
if offset != avail_numel:
|
||||||
|
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
||||||
|
|
||||||
|
print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
|
||||||
|
|
||||||
|
|
||||||
|
def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||||
|
exclude_frozen_parameters):
|
||||||
|
state_dict = OrderedDict()
|
||||||
|
|
||||||
|
# buffers
|
||||||
|
buffers = zero_model_states[0].buffers
|
||||||
|
state_dict.update(buffers)
|
||||||
|
if debug:
|
||||||
|
print(f"added {len(buffers)} buffers")
|
||||||
|
|
||||||
|
if not exclude_frozen_parameters:
|
||||||
|
_zero2_merge_frozen_params(state_dict, zero_model_states)
|
||||||
|
|
||||||
|
_zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
||||||
|
|
||||||
|
# recover shared parameters
|
||||||
|
for pair in zero_model_states[0].shared_params:
|
||||||
|
if pair[1] in state_dict:
|
||||||
|
state_dict[pair[0]] = state_dict[pair[1]]
|
||||||
|
|
||||||
|
return state_dict
|
||||||
|
|
||||||
|
|
||||||
|
def zero3_partitioned_param_info(unpartitioned_numel, world_size):
|
||||||
|
remainder = unpartitioned_numel % world_size
|
||||||
|
padding_numel = (world_size - remainder) if remainder else 0
|
||||||
|
partitioned_numel = math.ceil(unpartitioned_numel / world_size)
|
||||||
|
return partitioned_numel, padding_numel
|
||||||
|
|
||||||
|
|
||||||
|
def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
|
||||||
|
if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
|
||||||
|
return
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
for i in range(world_size):
|
||||||
|
num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
|
||||||
|
print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
|
||||||
|
|
||||||
|
frozen_param_shapes = zero_model_states[0].frozen_param_shapes
|
||||||
|
wanted_params = len(frozen_param_shapes)
|
||||||
|
wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
|
||||||
|
avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
|
||||||
|
print(f'Frozen params: Have {avail_numel} numels to process.')
|
||||||
|
print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
|
||||||
|
|
||||||
|
total_params = 0
|
||||||
|
total_numel = 0
|
||||||
|
for name, shape in zero_model_states[0].frozen_param_shapes.items():
|
||||||
|
total_params += 1
|
||||||
|
unpartitioned_numel = shape.numel()
|
||||||
|
total_numel += unpartitioned_numel
|
||||||
|
|
||||||
|
param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
|
||||||
|
state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
|
||||||
|
|
||||||
|
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(
|
||||||
|
f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
||||||
|
)
|
||||||
|
|
||||||
|
print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
|
||||||
|
|
||||||
|
|
||||||
|
def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
|
||||||
|
param_shapes = zero_model_states[0].param_shapes
|
||||||
|
avail_numel = fp32_flat_groups[0].numel() * world_size
|
||||||
|
# Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
|
||||||
|
# param, re-consolidating each param, while dealing with padding if any
|
||||||
|
|
||||||
|
# merge list of dicts, preserving order
|
||||||
|
param_shapes = {k: v for d in param_shapes for k, v in d.items()}
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
for i in range(world_size):
|
||||||
|
print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
|
||||||
|
|
||||||
|
wanted_params = len(param_shapes)
|
||||||
|
wanted_numel = sum(shape.numel() for shape in param_shapes.values())
|
||||||
|
# not asserting if there is a mismatch due to possible padding
|
||||||
|
avail_numel = fp32_flat_groups[0].numel() * world_size
|
||||||
|
print(f"Trainable params: Have {avail_numel} numels to process.")
|
||||||
|
print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
|
||||||
|
|
||||||
|
# params
|
||||||
|
# XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
|
||||||
|
# out-of-core computing solution
|
||||||
|
offset = 0
|
||||||
|
total_numel = 0
|
||||||
|
total_params = 0
|
||||||
|
for name, shape in param_shapes.items():
|
||||||
|
|
||||||
|
unpartitioned_numel = shape.numel()
|
||||||
|
total_numel += unpartitioned_numel
|
||||||
|
total_params += 1
|
||||||
|
|
||||||
|
partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
|
||||||
|
|
||||||
|
if debug:
|
||||||
|
print(
|
||||||
|
f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
|
||||||
|
)
|
||||||
|
|
||||||
|
# XXX: memory usage doubles here
|
||||||
|
state_dict[name] = torch.cat(
|
||||||
|
tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
|
||||||
|
0).narrow(0, 0, unpartitioned_numel).view(shape)
|
||||||
|
offset += partitioned_numel
|
||||||
|
|
||||||
|
offset *= world_size
|
||||||
|
|
||||||
|
# Sanity check
|
||||||
|
if offset != avail_numel:
|
||||||
|
raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
|
||||||
|
|
||||||
|
print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
|
||||||
|
|
||||||
|
|
||||||
|
def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
|
||||||
|
exclude_frozen_parameters):
|
||||||
|
state_dict = OrderedDict()
|
||||||
|
|
||||||
|
# buffers
|
||||||
|
buffers = zero_model_states[0].buffers
|
||||||
|
state_dict.update(buffers)
|
||||||
|
if debug:
|
||||||
|
print(f"added {len(buffers)} buffers")
|
||||||
|
|
||||||
|
if not exclude_frozen_parameters:
|
||||||
|
_zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
|
||||||
|
|
||||||
|
_zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
|
||||||
|
|
||||||
|
# recover shared parameters
|
||||||
|
for pair in zero_model_states[0].shared_params:
|
||||||
|
if pair[1] in state_dict:
|
||||||
|
state_dict[pair[0]] = state_dict[pair[1]]
|
||||||
|
|
||||||
|
return state_dict
|
||||||
|
|
||||||
|
|
||||||
|
def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
|
||||||
|
"""
|
||||||
|
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
|
||||||
|
``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
|
||||||
|
via a model hub.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
- ``checkpoint_dir``: path to the desired checkpoint folder
|
||||||
|
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
|
||||||
|
- ``exclude_frozen_parameters``: exclude frozen parameters
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- pytorch ``state_dict``
|
||||||
|
|
||||||
|
Note: this approach may not work if your application doesn't have sufficient free CPU memory and
|
||||||
|
you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
|
||||||
|
the checkpoint.
|
||||||
|
|
||||||
|
A typical usage might be ::
|
||||||
|
|
||||||
|
from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
|
||||||
|
# do the training and checkpoint saving
|
||||||
|
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
|
||||||
|
model = model.cpu() # move to cpu
|
||||||
|
model.load_state_dict(state_dict)
|
||||||
|
# submit to model hub or save the model to share with others
|
||||||
|
|
||||||
|
In this example the ``model`` will no longer be usable in the deepspeed context of the same
|
||||||
|
application. i.e. you will need to re-initialize the deepspeed engine, since
|
||||||
|
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
||||||
|
|
||||||
|
If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
|
||||||
|
|
||||||
|
"""
|
||||||
|
if tag is None:
|
||||||
|
latest_path = os.path.join(checkpoint_dir, 'latest')
|
||||||
|
if os.path.isfile(latest_path):
|
||||||
|
with open(latest_path, 'r') as fd:
|
||||||
|
tag = fd.read().strip()
|
||||||
|
else:
|
||||||
|
raise ValueError(f"Unable to find 'latest' file at {latest_path}")
|
||||||
|
|
||||||
|
ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
|
||||||
|
|
||||||
|
if not os.path.isdir(ds_checkpoint_dir):
|
||||||
|
raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
|
||||||
|
|
||||||
|
return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
|
||||||
|
|
||||||
|
|
||||||
|
def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
|
||||||
|
"""
|
||||||
|
Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
|
||||||
|
loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
||||||
|
- ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
|
||||||
|
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
||||||
|
- ``exclude_frozen_parameters``: exclude frozen parameters
|
||||||
|
"""
|
||||||
|
|
||||||
|
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
|
||||||
|
print(f"Saving fp32 state dict to {output_file}")
|
||||||
|
torch.save(state_dict, output_file)
|
||||||
|
|
||||||
|
|
||||||
|
def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
|
||||||
|
"""
|
||||||
|
1. Put the provided model to cpu
|
||||||
|
2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
|
||||||
|
3. Load it into the provided model
|
||||||
|
|
||||||
|
Args:
|
||||||
|
- ``model``: the model object to update
|
||||||
|
- ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
|
||||||
|
- ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- ``model`: modified model
|
||||||
|
|
||||||
|
Make sure you have plenty of CPU memory available before you call this function. If you don't
|
||||||
|
have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
|
||||||
|
conveniently placed for you in the checkpoint folder.
|
||||||
|
|
||||||
|
A typical usage might be ::
|
||||||
|
|
||||||
|
from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
|
||||||
|
model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
|
||||||
|
# submit to model hub or save the model to share with others
|
||||||
|
|
||||||
|
Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
|
||||||
|
of the same application. i.e. you will need to re-initialize the deepspeed engine, since
|
||||||
|
``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
|
||||||
|
|
||||||
|
"""
|
||||||
|
logger.info(f"Extracting fp32 weights")
|
||||||
|
state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
|
||||||
|
|
||||||
|
logger.info(f"Overwriting model with fp32 weights")
|
||||||
|
model = model.cpu()
|
||||||
|
model.load_state_dict(state_dict, strict=False)
|
||||||
|
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == "__main__":
|
||||||
|
|
||||||
|
parser = argparse.ArgumentParser()
|
||||||
|
parser.add_argument("checkpoint_dir",
|
||||||
|
type=str,
|
||||||
|
help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
|
||||||
|
parser.add_argument(
|
||||||
|
"output_file",
|
||||||
|
type=str,
|
||||||
|
help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
|
||||||
|
parser.add_argument("-t",
|
||||||
|
"--tag",
|
||||||
|
type=str,
|
||||||
|
default=None,
|
||||||
|
help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
|
||||||
|
parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
|
||||||
|
parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
|
debug = args.debug
|
||||||
|
|
||||||
|
convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
|
||||||
|
args.output_file,
|
||||||
|
tag=args.tag,
|
||||||
|
exclude_frozen_parameters=args.exclude_frozen_parameters)
|
||||||
Reference in New Issue
Block a user