初始化项目，由ModelHub XC社区提供模型
Model: coder3101/Qwen2.5-VL-3B-Instruct-heretic 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
|
||||
555
README.md
Normal file
555
README.md
Normal file
@@ -0,0 +1,555 @@
|
||||
---
|
||||
license_name: qwen-research
|
||||
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
|
||||
language:
|
||||
- en
|
||||
pipeline_tag: image-text-to-text
|
||||
tags:
|
||||
- multimodal
|
||||
- heretic
|
||||
- uncensored
|
||||
- decensored
|
||||
- abliterated
|
||||
library_name: transformers
|
||||
base_model:
|
||||
- Qwen/Qwen2.5-VL-3B-Instruct
|
||||
---
|
||||
# This is a decensored version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct), made using [Heretic](https://github.com/p-e-w/heretic) v1.1.0
|
||||
|
||||
## Abliteration parameters
|
||||
|
||||
| Parameter | Value |
|
||||
| :-------- | :---: |
|
||||
| **direction_index** | 26.01 |
|
||||
| **attn.o_proj.max_weight** | 1.49 |
|
||||
| **attn.o_proj.max_weight_position** | 25.31 |
|
||||
| **attn.o_proj.min_weight** | 0.62 |
|
||||
| **attn.o_proj.min_weight_distance** | 16.79 |
|
||||
| **mlp.down_proj.max_weight** | 0.94 |
|
||||
| **mlp.down_proj.max_weight_position** | 21.66 |
|
||||
| **mlp.down_proj.min_weight** | 0.64 |
|
||||
| **mlp.down_proj.min_weight_distance** | 5.53 |
|
||||
|
||||
## Performance
|
||||
|
||||
| Metric | This model | Original model ([Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)) |
|
||||
| :----- | :--------: | :---------------------------: |
|
||||
| **KL divergence** | 0.0103 | 0 *(by definition)* |
|
||||
| **Refusals** | 5/100 | 99/100 |
|
||||
|
||||
-----
|
||||
|
||||
|
||||
# Qwen2.5-VL-3B-Instruct
|
||||
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
|
||||
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
|
||||
</a>
|
||||
|
||||
## Introduction
|
||||
|
||||
In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
|
||||
|
||||
#### Key Enhancements:
|
||||
* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
|
||||
|
||||
* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
|
||||
|
||||
* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
|
||||
|
||||
* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
|
||||
|
||||
* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
|
||||
|
||||
|
||||
#### Model Architecture Updates:
|
||||
|
||||
* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
|
||||
|
||||
We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
|
||||
|
||||
<p align="center">
|
||||
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
|
||||
<p>
|
||||
|
||||
|
||||
* **Streamlined and Efficient Vision Encoder**
|
||||
|
||||
We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
|
||||
|
||||
|
||||
We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
|
||||
|
||||
|
||||
|
||||
## Evaluation
|
||||
|
||||
### Image benchmark
|
||||
|
||||
| Benchmark | InternVL2.5-4B |Qwen2-VL-7B |Qwen2.5-VL-3B |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| MMMU<sub>val</sub> | 52.3 | 54.1 | 53.1|
|
||||
| MMMU-Pro<sub>val</sub> | **32.7** | 30.5 | 31.6|
|
||||
| AI2D<sub>test</sub> | 81.4 | **83.0** | 81.5 |
|
||||
| DocVQA<sub>test</sub> | 91.6 | 94.5 | **93.9** |
|
||||
| InfoVQA<sub>test</sub> | 72.1 | 76.5 | **77.1** |
|
||||
| TextVQA<sub>val</sub> | 76.8 | **84.3** | 79.3|
|
||||
| MMBench-V1.1<sub>test</sub> | 79.3 | **80.7** | 77.6 |
|
||||
| MMStar | 58.3 | **60.7** | 55.9 |
|
||||
| MathVista<sub>testmini</sub> | 60.5 | 58.2 | **62.3** |
|
||||
| MathVision<sub>full</sub> | 20.9 | 16.3 | **21.2** |
|
||||
|
||||
|
||||
### Video benchmark
|
||||
| Benchmark | InternVL2.5-4B | Qwen2-VL-7B | Qwen2.5-VL-3B |
|
||||
| :--- | :---: | :---: | :---: |
|
||||
| MVBench | 71.6 | 67.0 | 67.0 |
|
||||
| VideoMME | 63.6/62.3 | 69.0/63.3 | 67.6/61.5 |
|
||||
| MLVU | 48.3 | - | 68.2 |
|
||||
| LVBench | - | - | 43.3 |
|
||||
| MMBench-Video | 1.73 | 1.44 | 1.63 |
|
||||
| EgoSchema | - | - | 64.8 |
|
||||
| PerceptionTest | - | - | 66.9 |
|
||||
| TempCompass | - | - | 64.4 |
|
||||
| LongVideoBench | 55.2 | 55.6 | 54.2 |
|
||||
| CharadesSTA/mIoU | - | - | 38.8 |
|
||||
|
||||
|
||||
### Agent benchmark
|
||||
| Benchmarks | Qwen2.5-VL-3B |
|
||||
|-------------------------|---------------|
|
||||
| ScreenSpot | 55.5 |
|
||||
| ScreenSpot Pro | 23.9 |
|
||||
| AITZ_EM | 76.9 |
|
||||
| Android Control High_EM | 63.7 |
|
||||
| Android Control Low_EM | 22.2 |
|
||||
| AndroidWorld_SR | 90.8 |
|
||||
| MobileMiniWob++_SR | 67.9 |
|
||||
|
||||
## Requirements
|
||||
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
|
||||
```
|
||||
pip install git+https://github.com/huggingface/transformers accelerate
|
||||
```
|
||||
or you might encounter the following error:
|
||||
```
|
||||
KeyError: 'qwen2_5_vl'
|
||||
```
|
||||
|
||||
|
||||
## Quickstart
|
||||
|
||||
Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
|
||||
|
||||
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
|
||||
```
|
||||
pip install git+https://github.com/huggingface/transformers accelerate
|
||||
```
|
||||
or you might encounter the following error:
|
||||
```
|
||||
KeyError: 'qwen2_5_vl'
|
||||
```
|
||||
|
||||
|
||||
We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
|
||||
|
||||
```bash
|
||||
# It's highly recommanded to use `[decord]` feature for faster video loading.
|
||||
pip install qwen-vl-utils[decord]==0.0.8
|
||||
```
|
||||
|
||||
If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
|
||||
|
||||
### Using 🤗 Transformers to Chat
|
||||
|
||||
Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
|
||||
|
||||
```python
|
||||
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
|
||||
from qwen_vl_utils import process_vision_info
|
||||
|
||||
# default: Load the model on the available device(s)
|
||||
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
"Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
|
||||
)
|
||||
|
||||
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
|
||||
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
|
||||
# "Qwen/Qwen2.5-VL-3B-Instruct",
|
||||
# torch_dtype=torch.bfloat16,
|
||||
# attn_implementation="flash_attention_2",
|
||||
# device_map="auto",
|
||||
# )
|
||||
|
||||
# default processer
|
||||
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
|
||||
|
||||
# The default range for the number of visual tokens per image in the model is 4-16384.
|
||||
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
|
||||
# min_pixels = 256*28*28
|
||||
# max_pixels = 1280*28*28
|
||||
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
|
||||
},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
# Preparation for inference
|
||||
text = processor.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
image_inputs, video_inputs = process_vision_info(messages)
|
||||
inputs = processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
videos=video_inputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
inputs = inputs.to("cuda")
|
||||
|
||||
# Inference: Generation of the output
|
||||
generated_ids = model.generate(**inputs, max_new_tokens=128)
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
output_text = processor.batch_decode(
|
||||
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
||||
)
|
||||
print(output_text)
|
||||
```
|
||||
<details>
|
||||
<summary>Multi image inference</summary>
|
||||
|
||||
```python
|
||||
# Messages containing multiple images and a text query
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "image": "file:///path/to/image1.jpg"},
|
||||
{"type": "image", "image": "file:///path/to/image2.jpg"},
|
||||
{"type": "text", "text": "Identify the similarities between these images."},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
# Preparation for inference
|
||||
text = processor.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
image_inputs, video_inputs = process_vision_info(messages)
|
||||
inputs = processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
videos=video_inputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
inputs = inputs.to("cuda")
|
||||
|
||||
# Inference
|
||||
generated_ids = model.generate(**inputs, max_new_tokens=128)
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
output_text = processor.batch_decode(
|
||||
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
||||
)
|
||||
print(output_text)
|
||||
```
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Video inference</summary>
|
||||
|
||||
```python
|
||||
# Messages containing a images list as a video and a text query
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "video",
|
||||
"video": [
|
||||
"file:///path/to/frame1.jpg",
|
||||
"file:///path/to/frame2.jpg",
|
||||
"file:///path/to/frame3.jpg",
|
||||
"file:///path/to/frame4.jpg",
|
||||
],
|
||||
},
|
||||
{"type": "text", "text": "Describe this video."},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
# Messages containing a local video path and a text query
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "video",
|
||||
"video": "file:///path/to/video1.mp4",
|
||||
"max_pixels": 360 * 420,
|
||||
"fps": 1.0,
|
||||
},
|
||||
{"type": "text", "text": "Describe this video."},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
# Messages containing a video url and a text query
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "video",
|
||||
"video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
|
||||
},
|
||||
{"type": "text", "text": "Describe this video."},
|
||||
],
|
||||
}
|
||||
]
|
||||
|
||||
#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
|
||||
# Preparation for inference
|
||||
text = processor.apply_chat_template(
|
||||
messages, tokenize=False, add_generation_prompt=True
|
||||
)
|
||||
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
|
||||
inputs = processor(
|
||||
text=[text],
|
||||
images=image_inputs,
|
||||
videos=video_inputs,
|
||||
fps=fps,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
**video_kwargs,
|
||||
)
|
||||
inputs = inputs.to("cuda")
|
||||
|
||||
# Inference
|
||||
generated_ids = model.generate(**inputs, max_new_tokens=128)
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
output_text = processor.batch_decode(
|
||||
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
||||
)
|
||||
print(output_text)
|
||||
```
|
||||
|
||||
Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
|
||||
|
||||
| Backend | HTTP | HTTPS |
|
||||
|-------------|------|-------|
|
||||
| torchvision >= 0.19.0 | ✅ | ✅ |
|
||||
| torchvision < 0.19.0 | ❌ | ❌ |
|
||||
| decord | ✅ | ❌ |
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Batch inference</summary>
|
||||
|
||||
```python
|
||||
# Sample messages for batch inference
|
||||
messages1 = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "image": "file:///path/to/image1.jpg"},
|
||||
{"type": "image", "image": "file:///path/to/image2.jpg"},
|
||||
{"type": "text", "text": "What are the common elements in these pictures?"},
|
||||
],
|
||||
}
|
||||
]
|
||||
messages2 = [
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Who are you?"},
|
||||
]
|
||||
# Combine messages for batch processing
|
||||
messages = [messages1, messages2]
|
||||
|
||||
# Preparation for batch inference
|
||||
texts = [
|
||||
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
|
||||
for msg in messages
|
||||
]
|
||||
image_inputs, video_inputs = process_vision_info(messages)
|
||||
inputs = processor(
|
||||
text=texts,
|
||||
images=image_inputs,
|
||||
videos=video_inputs,
|
||||
padding=True,
|
||||
return_tensors="pt",
|
||||
)
|
||||
inputs = inputs.to("cuda")
|
||||
|
||||
# Batch Inference
|
||||
generated_ids = model.generate(**inputs, max_new_tokens=128)
|
||||
generated_ids_trimmed = [
|
||||
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
|
||||
]
|
||||
output_texts = processor.batch_decode(
|
||||
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
|
||||
)
|
||||
print(output_texts)
|
||||
```
|
||||
</details>
|
||||
|
||||
### 🤖 ModelScope
|
||||
We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
|
||||
|
||||
|
||||
### More Usage Tips
|
||||
|
||||
For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
|
||||
|
||||
```python
|
||||
# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
|
||||
## Local file path
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "image": "file:///path/to/your/image.jpg"},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
## Image URL
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "image": "http://path/to/your/image.jpg"},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
## Base64 encoded image
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{"type": "image", "image": "data:image;base64,/9j/..."},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
```
|
||||
#### Image Resolution for performance boost
|
||||
|
||||
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
|
||||
|
||||
```python
|
||||
min_pixels = 256 * 28 * 28
|
||||
max_pixels = 1280 * 28 * 28
|
||||
processor = AutoProcessor.from_pretrained(
|
||||
"Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
|
||||
)
|
||||
```
|
||||
|
||||
Besides, We provide two methods for fine-grained control over the image size input to the model:
|
||||
|
||||
1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
|
||||
|
||||
2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
|
||||
|
||||
```python
|
||||
# min_pixels and max_pixels
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"image": "file:///path/to/your/image.jpg",
|
||||
"resized_height": 280,
|
||||
"resized_width": 420,
|
||||
},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
# resized_height and resized_width
|
||||
messages = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": [
|
||||
{
|
||||
"type": "image",
|
||||
"image": "file:///path/to/your/image.jpg",
|
||||
"min_pixels": 50176,
|
||||
"max_pixels": 50176,
|
||||
},
|
||||
{"type": "text", "text": "Describe this image."},
|
||||
],
|
||||
}
|
||||
]
|
||||
```
|
||||
|
||||
### Processing Long Texts
|
||||
|
||||
The current `config.json` is set for context length up to 32,768 tokens.
|
||||
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
|
||||
|
||||
For supported frameworks, you could add the following to `config.json` to enable YaRN:
|
||||
|
||||
```
|
||||
{
|
||||
...,
|
||||
"type": "yarn",
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"factor": 4,
|
||||
"original_max_position_embeddings": 32768
|
||||
}
|
||||
```
|
||||
|
||||
However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
|
||||
|
||||
At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
|
||||
|
||||
|
||||
|
||||
## Citation
|
||||
|
||||
If you find our work helpful, feel free to give us a cite.
|
||||
|
||||
```
|
||||
@misc{qwen2.5-VL,
|
||||
title = {Qwen2.5-VL},
|
||||
url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
|
||||
author = {Qwen Team},
|
||||
month = {January},
|
||||
year = {2025}
|
||||
}
|
||||
|
||||
@article{Qwen2VL,
|
||||
title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
|
||||
author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
|
||||
journal={arXiv preprint arXiv:2409.12191},
|
||||
year={2024}
|
||||
}
|
||||
|
||||
@article{Qwen-VL,
|
||||
title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
|
||||
author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
|
||||
journal={arXiv preprint arXiv:2308.12966},
|
||||
year={2023}
|
||||
}
|
||||
```
|
||||
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
|
||||
}
|
||||
7
chat_template.jinja
Normal file
7
chat_template.jinja
Normal file
@@ -0,0 +1,7 @@
|
||||
{% 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
|
||||
You are a helpful assistant.<|im_end|>
|
||||
{% endif %}<|im_start|>{{ message['role'] }}
|
||||
{% if message['content'] is string %}{{ message['content'] }}<|im_end|>
|
||||
{% 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|>
|
||||
{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant
|
||||
{% endif %}
|
||||
140
config.json
Normal file
140
config.json
Normal file
@@ -0,0 +1,140 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "bfloat16",
|
||||
"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,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": 32768,
|
||||
"text_config": {
|
||||
"_name_or_path": "Qwen/Qwen2.5-VL-3B-Instruct",
|
||||
"architectures": [
|
||||
"Qwen2_5_VLForConditionalGeneration"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "bfloat16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2048,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 11008,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 128000,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2_5_vl_text",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 2,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": {
|
||||
"mrope_section": [
|
||||
16,
|
||||
24,
|
||||
24
|
||||
],
|
||||
"rope_type": "default",
|
||||
"type": "default"
|
||||
},
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vision_token_id": 151654,
|
||||
"vocab_size": 151936
|
||||
},
|
||||
"transformers_version": "4.57.3",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"video_token_id": 151656,
|
||||
"vision_config": {
|
||||
"depth": 32,
|
||||
"dtype": "bfloat16",
|
||||
"fullatt_block_indexes": [
|
||||
7,
|
||||
15,
|
||||
23,
|
||||
31
|
||||
],
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1280,
|
||||
"in_channels": 3,
|
||||
"in_chans": 3,
|
||||
"initializer_range": 0.02,
|
||||
"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,
|
||||
"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": "4.57.3"
|
||||
}
|
||||
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:dd9312956e3ab8c7fbafc6fa86a1a33f5b0dc74f6011935ae0485b070af86bd7
|
||||
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 sha256:40091d54716577e5b017167a692940c393832f11d6cf9f4b53507a5adf11e3ef
|
||||
size 2511587184
|
||||
832
model.safetensors.index.json
Normal file
832
model.safetensors.index.json
Normal file
@@ -0,0 +1,832 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 3754622976,
|
||||
"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
|
||||
207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
"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,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user