Add <action> token and update config
Browse files- README.md +270 -0
- added_tokens.json +131 -0
- chat_template.jinja +2 -0
- chat_template.json +3 -0
- config.json +141 -0
- generation_config.json +7 -0
- merges.txt +0 -0
- model.safetensors +3 -0
- onnx/decoder_model_merged.onnx +3 -0
- onnx/decoder_model_merged_bnb4.onnx +3 -0
- onnx/decoder_model_merged_fp16.onnx +3 -0
- onnx/decoder_model_merged_int8.onnx +3 -0
- onnx/decoder_model_merged_q4.onnx +3 -0
- onnx/decoder_model_merged_q4f16.onnx +3 -0
- onnx/decoder_model_merged_quantized.onnx +3 -0
- onnx/decoder_model_merged_uint8.onnx +3 -0
- onnx/embed_tokens.onnx +3 -0
- onnx/embed_tokens_bnb4.onnx +3 -0
- onnx/embed_tokens_fp16.onnx +3 -0
- onnx/embed_tokens_int8.onnx +3 -0
- onnx/embed_tokens_q4.onnx +3 -0
- onnx/embed_tokens_q4f16.onnx +3 -0
- onnx/embed_tokens_quantized.onnx +3 -0
- onnx/embed_tokens_uint8.onnx +3 -0
- onnx/vision_encoder.onnx +3 -0
- onnx/vision_encoder_bnb4.onnx +3 -0
- onnx/vision_encoder_fp16.onnx +3 -0
- onnx/vision_encoder_int8.onnx +3 -0
- onnx/vision_encoder_q4.onnx +3 -0
- onnx/vision_encoder_q4f16.onnx +3 -0
- onnx/vision_encoder_quantized.onnx +3 -0
- onnx/vision_encoder_uint8.onnx +3 -0
- preprocessor_config.json +35 -0
- processor_config.json +4 -0
- special_tokens_map.json +43 -0
- tokenizer.json +0 -0
- tokenizer_config.json +1197 -0
- vocab.json +0 -0
README.md
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1 |
+
---
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library_name: transformers
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license: apache-2.0
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datasets:
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+
- HuggingFaceM4/the_cauldron
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- HuggingFaceM4/Docmatix
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- lmms-lab/LLaVA-OneVision-Data
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+
- lmms-lab/M4-Instruct-Data
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+
- HuggingFaceFV/finevideo
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- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
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+
- lmms-lab/LLaVA-Video-178K
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+
- orrzohar/Video-STaR
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+
- Mutonix/Vript
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+
- TIGER-Lab/VISTA-400K
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+
- Enxin/MovieChat-1K_train
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- ShareGPT4Video/ShareGPT4Video
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+
pipeline_tag: image-text-to-text
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+
language:
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+
- en
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base_model:
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- HuggingFaceTB/SmolVLM-500M-Instruct
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+
---
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23 |
+
|
24 |
+
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM2_banner.png" width="800" height="auto" alt="Image description">
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+
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+
# SmolVLM2-500M-Video
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27 |
+
|
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+
SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited.
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## Model Summary
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30 |
+
|
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+
- **Developed by:** Hugging Face 🤗
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- **Model type:** Multi-modal model (image/multi-image/video/text)
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+
- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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+
- **Architecture:** Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary)
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+
|
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+
## Resources
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+
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+
- **Demo:** [Video Highlight Generator](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator)
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- **Blog:** [Blog post](https://huggingface.co/blog/smolvlm2)
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+
|
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+
## Uses
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43 |
+
|
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+
SmolVLM2 can be used for inference on multimodal (video / image / text) tasks where the input consists of text queries along with video or one or more images. Text and media files can be interleaved arbitrarily, enabling tasks like captioning, visual question answering, and storytelling based on visual content. The model does not support image or video generation.
|
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+
|
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+
To fine-tune SmolVLM2 on a specific task, you can follow [the fine-tuning tutorial](https://github.com/huggingface/smollm/blob/main/vision/finetuning/Smol_VLM_FT.ipynb).
|
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+
|
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## Evaluation
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+
|
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We evaluated the performance of the SmolVLM2 family on the following scientific benchmarks:
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+
|
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+
| Size | Video-MME | MLVU | MVBench |
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|----------|-----------------|----------|---------------|
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| 2.2B | 52.1 | 55.2 | 46.27 |
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| 500M | 42.2 | 47.3 | 39.73 |
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| 256M | 33.7 | 40.6 | 32.7 |
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|
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+
|
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+
### How to get started
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+
|
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+
You can use transformers to load, infer and fine-tune SmolVLM. Make sure you have num2words, flash-attn and latest transformers installed.
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+
You can load the model as follows.
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+
|
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+
```python
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from transformers import AutoProcessor, AutoModelForImageTextToText
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import torch
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+
|
68 |
+
model_path = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
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69 |
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processor = AutoProcessor.from_pretrained(model_path)
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+
model = AutoModelForImageTextToText.from_pretrained(
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71 |
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model_path,
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+
torch_dtype=torch.bfloat16,
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+
_attn_implementation="flash_attention_2"
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).to("cuda")
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```
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+
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#### Simple Inference
|
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+
|
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You preprocess your inputs directly using chat templates and directly passing them
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+
|
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+
```python
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82 |
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messages = [
|
83 |
+
{
|
84 |
+
"role": "user",
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85 |
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"content": [
|
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{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
|
87 |
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{"type": "text", "text": "Can you describe this image?"},
|
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]
|
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},
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]
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|
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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+
return_dict=True,
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return_tensors="pt",
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).to(model.device, dtype=torch.bfloat16)
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+
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generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
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generated_texts = processor.batch_decode(
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generated_ids,
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skip_special_tokens=True,
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)
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print(generated_texts[0])
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```
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|
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#### Video Inference
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+
|
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To use SmolVLM2 for video inference, make sure you have decord installed.
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+
|
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+
```python
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messages = [
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{
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"role": "user",
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"content": [
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{"type": "video", "path": "path_to_video.mp4"},
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{"type": "text", "text": "Describe this video in detail"}
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]
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},
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]
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device, dtype=torch.bfloat16)
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+
|
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+
generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
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generated_texts = processor.batch_decode(
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generated_ids,
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skip_special_tokens=True,
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)
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print(generated_texts[0])
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```
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#### Multi-image Interleaved Inference
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+
|
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You can interleave multiple media with text using chat templates.
|
142 |
+
|
143 |
+
```python
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import torch
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+
|
146 |
+
|
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+
messages = [
|
148 |
+
{
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+
"role": "user",
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150 |
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"content": [
|
151 |
+
{"type": "text", "text": "What is the similarity between these two images?"},
|
152 |
+
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
|
153 |
+
{"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"},
|
154 |
+
]
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155 |
+
},
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+
]
|
157 |
+
|
158 |
+
inputs = processor.apply_chat_template(
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+
messages,
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+
add_generation_prompt=True,
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+
tokenize=True,
|
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+
return_dict=True,
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163 |
+
return_tensors="pt",
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+
).to(model.device, dtype=torch.bfloat16)
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+
|
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+
generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
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167 |
+
generated_texts = processor.batch_decode(
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+
generated_ids,
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+
skip_special_tokens=True,
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+
)
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+
print(generated_texts[0])
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+
```
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+
|
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+
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+
### Model optimizations
|
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+
|
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+
## Misuse and Out-of-scope Use
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+
|
179 |
+
SmolVLM is not intended for high-stakes scenarios or critical decision-making processes that affect an individual's well-being or livelihood. The model may produce content that appears factual but may not be accurate. Misuse includes, but is not limited to:
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+
|
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+
- Prohibited Uses:
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182 |
+
- Evaluating or scoring individuals (e.g., in employment, education, credit)
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+
- Critical automated decision-making
|
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+
- Generating unreliable factual content
|
185 |
+
- Malicious Activities:
|
186 |
+
- Spam generation
|
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+
- Disinformation campaigns
|
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+
- Harassment or abuse
|
189 |
+
- Unauthorized surveillance
|
190 |
+
|
191 |
+
### License
|
192 |
+
|
193 |
+
SmolVLM2 is built upon [SigLIP](https://huggingface.co/google/siglip-base-patch16-512) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) for text decoder part.
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+
|
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+
We release the SmolVLM2 checkpoints under the Apache 2.0 license.
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+
|
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+
## Citation information
|
198 |
+
You can cite us in the following way:
|
199 |
+
```bibtex
|
200 |
+
@article{marafioti2025smolvlm,
|
201 |
+
title={SmolVLM: Redefining small and efficient multimodal models},
|
202 |
+
author={Andrés Marafioti and Orr Zohar and Miquel Farré and Merve Noyan and Elie Bakouch and Pedro Cuenca and Cyril Zakka and Loubna Ben Allal and Anton Lozhkov and Nouamane Tazi and Vaibhav Srivastav and Joshua Lochner and Hugo Larcher and Mathieu Morlon and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
|
203 |
+
journal={arXiv preprint arXiv:2504.05299},
|
204 |
+
year={2025}
|
205 |
+
}
|
206 |
+
```
|
207 |
+
|
208 |
+
## Training Data
|
209 |
+
SmolVLM2 used 3.3M samples for training originally from ten different datasets: [LlaVa Onevision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), [M4-Instruct](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data), [Mammoth](https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M), [LlaVa Video 178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K), [FineVideo](https://huggingface.co/datasets/HuggingFaceFV/finevideo), [VideoStar](https://huggingface.co/datasets/orrzohar/Video-STaR), [VRipt](https://huggingface.co/datasets/Mutonix/Vript), [Vista-400K](https://huggingface.co/datasets/TIGER-Lab/VISTA-400K), [MovieChat](https://huggingface.co/datasets/Enxin/MovieChat-1K_train) and [ShareGPT4Video](https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video).
|
210 |
+
In the following plots we give a general overview of the samples across modalities and the source of those samples.
|
211 |
+
<!--
|
212 |
+
<center><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_data_split.png" width="auto" height="auto" alt="Image description">
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+
</center>
|
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+
|
215 |
+
### Details
|
216 |
+
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_datadetails.png" width="auto" height="auto" alt="Image description"> -->
|
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+
|
218 |
+
## Data Split per modality
|
219 |
+
|
220 |
+
| Data Type | Percentage |
|
221 |
+
|--------------|------------|
|
222 |
+
| Image | 34.4% |
|
223 |
+
| Text | 20.2% |
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+
| Video | 33.0% |
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+
| Multi-image | 12.3% |
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+
|
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+
|
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+
## Granular dataset slices per modality
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+
|
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+
### Text Datasets
|
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+
| Dataset | Percentage |
|
232 |
+
|--------------------------------------------|------------|
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+
| llava-onevision/magpie_pro_ft3_80b_mt | 6.8% |
|
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+
| llava-onevision/magpie_pro_ft3_80b_tt | 6.8% |
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| llava-onevision/magpie_pro_qwen2_72b_tt | 5.8% |
|
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+
| llava-onevision/mathqa | 0.9% |
|
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+
|
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+
### Multi-image Datasets
|
239 |
+
| Dataset | Percentage |
|
240 |
+
|--------------------------------------------|------------|
|
241 |
+
| m4-instruct-data/m4_instruct_multiimage | 10.4% |
|
242 |
+
| mammoth/multiimage-cap6 | 1.9% |
|
243 |
+
|
244 |
+
### Image Datasets
|
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+
| Dataset | Percentage |
|
246 |
+
|--------------------------------------------|------------|
|
247 |
+
| llava-onevision/other | 17.4% |
|
248 |
+
| llava-onevision/vision_flan | 3.9% |
|
249 |
+
| llava-onevision/mavis_math_metagen | 2.6% |
|
250 |
+
| llava-onevision/mavis_math_rule_geo | 2.5% |
|
251 |
+
| llava-onevision/sharegpt4o | 1.7% |
|
252 |
+
| llava-onevision/sharegpt4v_coco | 1.5% |
|
253 |
+
| llava-onevision/image_textualization | 1.3% |
|
254 |
+
| llava-onevision/sharegpt4v_llava | 0.9% |
|
255 |
+
| llava-onevision/mapqa | 0.9% |
|
256 |
+
| llava-onevision/qa | 0.8% |
|
257 |
+
| llava-onevision/textocr | 0.8% |
|
258 |
+
|
259 |
+
### Video Datasets
|
260 |
+
| Dataset | Percentage |
|
261 |
+
|--------------------------------------------|------------|
|
262 |
+
| llava-video-178k/1-2m | 7.3% |
|
263 |
+
| llava-video-178k/2-3m | 7.0% |
|
264 |
+
| other-video/combined | 5.7% |
|
265 |
+
| llava-video-178k/hound | 4.4% |
|
266 |
+
| llava-video-178k/0-30s | 2.4% |
|
267 |
+
| video-star/starb | 2.2% |
|
268 |
+
| vista-400k/combined | 2.2% |
|
269 |
+
| vript/long | 1.0% |
|
270 |
+
| ShareGPT4Video/all | 0.8% |
|
added_tokens.json
ADDED
@@ -0,0 +1,131 @@
|
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|
|
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|
|
|
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|
|
|
|
|
1 |
+
{
|
2 |
+
"<action>": 49280,
|
3 |
+
"<end_of_utterance>": 49279,
|
4 |
+
"<fake_token_around_image>": 49189,
|
5 |
+
"<global-img>": 49152,
|
6 |
+
"<image>": 49190,
|
7 |
+
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|
8 |
+
"<row_1_col_2>": 49154,
|
9 |
+
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|
10 |
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|
11 |
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|
12 |
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|
13 |
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"<row_2_col_1>": 49159,
|
14 |
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|
15 |
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|
16 |
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|
17 |
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|
18 |
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"<row_2_col_6>": 49164,
|
19 |
+
"<row_3_col_1>": 49165,
|
20 |
+
"<row_3_col_2>": 49166,
|
21 |
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"<row_3_col_3>": 49167,
|
22 |
+
"<row_3_col_4>": 49168,
|
23 |
+
"<row_3_col_5>": 49169,
|
24 |
+
"<row_3_col_6>": 49170,
|
25 |
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"<row_4_col_1>": 49171,
|
26 |
+
"<row_4_col_2>": 49172,
|
27 |
+
"<row_4_col_3>": 49173,
|
28 |
+
"<row_4_col_4>": 49174,
|
29 |
+
"<row_4_col_5>": 49175,
|
30 |
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|
31 |
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|
32 |
+
"<row_5_col_2>": 49178,
|
33 |
+
"<row_5_col_3>": 49179,
|
34 |
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|
35 |
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|
36 |
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|
37 |
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|
38 |
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|
39 |
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|
40 |
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|
41 |
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|
42 |
+
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|
43 |
+
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|
44 |
+
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|
45 |
+
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|
46 |
+
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|
47 |
+
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|
48 |
+
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|
49 |
+
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|
50 |
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|
51 |
+
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|
52 |
+
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|
53 |
+
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|
54 |
+
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|
55 |
+
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|
56 |
+
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|
57 |
+
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|
58 |
+
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|
59 |
+
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|
60 |
+
"<|reserved_special_token_25|>": 49216,
|
61 |
+
"<|reserved_special_token_26|>": 49217,
|
62 |
+
"<|reserved_special_token_27|>": 49218,
|
63 |
+
"<|reserved_special_token_28|>": 49219,
|
64 |
+
"<|reserved_special_token_29|>": 49220,
|
65 |
+
"<|reserved_special_token_2|>": 49193,
|
66 |
+
"<|reserved_special_token_30|>": 49221,
|
67 |
+
"<|reserved_special_token_31|>": 49222,
|
68 |
+
"<|reserved_special_token_32|>": 49223,
|
69 |
+
"<|reserved_special_token_33|>": 49224,
|
70 |
+
"<|reserved_special_token_34|>": 49225,
|
71 |
+
"<|reserved_special_token_35|>": 49226,
|
72 |
+
"<|reserved_special_token_36|>": 49227,
|
73 |
+
"<|reserved_special_token_37|>": 49228,
|
74 |
+
"<|reserved_special_token_38|>": 49229,
|
75 |
+
"<|reserved_special_token_39|>": 49230,
|
76 |
+
"<|reserved_special_token_3|>": 49194,
|
77 |
+
"<|reserved_special_token_40|>": 49231,
|
78 |
+
"<|reserved_special_token_41|>": 49232,
|
79 |
+
"<|reserved_special_token_42|>": 49233,
|
80 |
+
"<|reserved_special_token_43|>": 49234,
|
81 |
+
"<|reserved_special_token_44|>": 49235,
|
82 |
+
"<|reserved_special_token_45|>": 49236,
|
83 |
+
"<|reserved_special_token_46|>": 49237,
|
84 |
+
"<|reserved_special_token_47|>": 49238,
|
85 |
+
"<|reserved_special_token_48|>": 49239,
|
86 |
+
"<|reserved_special_token_49|>": 49240,
|
87 |
+
"<|reserved_special_token_4|>": 49195,
|
88 |
+
"<|reserved_special_token_50|>": 49241,
|
89 |
+
"<|reserved_special_token_51|>": 49242,
|
90 |
+
"<|reserved_special_token_52|>": 49243,
|
91 |
+
"<|reserved_special_token_53|>": 49244,
|
92 |
+
"<|reserved_special_token_54|>": 49245,
|
93 |
+
"<|reserved_special_token_55|>": 49246,
|
94 |
+
"<|reserved_special_token_56|>": 49247,
|
95 |
+
"<|reserved_special_token_57|>": 49248,
|
96 |
+
"<|reserved_special_token_58|>": 49249,
|
97 |
+
"<|reserved_special_token_59|>": 49250,
|
98 |
+
"<|reserved_special_token_5|>": 49196,
|
99 |
+
"<|reserved_special_token_60|>": 49251,
|
100 |
+
"<|reserved_special_token_61|>": 49252,
|
101 |
+
"<|reserved_special_token_62|>": 49253,
|
102 |
+
"<|reserved_special_token_63|>": 49254,
|
103 |
+
"<|reserved_special_token_64|>": 49255,
|
104 |
+
"<|reserved_special_token_65|>": 49256,
|
105 |
+
"<|reserved_special_token_66|>": 49257,
|
106 |
+
"<|reserved_special_token_67|>": 49258,
|
107 |
+
"<|reserved_special_token_68|>": 49259,
|
108 |
+
"<|reserved_special_token_69|>": 49260,
|
109 |
+
"<|reserved_special_token_6|>": 49197,
|
110 |
+
"<|reserved_special_token_70|>": 49261,
|
111 |
+
"<|reserved_special_token_71|>": 49262,
|
112 |
+
"<|reserved_special_token_72|>": 49263,
|
113 |
+
"<|reserved_special_token_73|>": 49264,
|
114 |
+
"<|reserved_special_token_74|>": 49265,
|
115 |
+
"<|reserved_special_token_75|>": 49266,
|
116 |
+
"<|reserved_special_token_76|>": 49267,
|
117 |
+
"<|reserved_special_token_77|>": 49268,
|
118 |
+
"<|reserved_special_token_78|>": 49269,
|
119 |
+
"<|reserved_special_token_79|>": 49270,
|
120 |
+
"<|reserved_special_token_7|>": 49198,
|
121 |
+
"<|reserved_special_token_80|>": 49271,
|
122 |
+
"<|reserved_special_token_81|>": 49272,
|
123 |
+
"<|reserved_special_token_82|>": 49273,
|
124 |
+
"<|reserved_special_token_83|>": 49274,
|
125 |
+
"<|reserved_special_token_84|>": 49275,
|
126 |
+
"<|reserved_special_token_85|>": 49276,
|
127 |
+
"<|reserved_special_token_86|>": 49277,
|
128 |
+
"<|reserved_special_token_87|>": 49278,
|
129 |
+
"<|reserved_special_token_8|>": 49199,
|
130 |
+
"<|reserved_special_token_9|>": 49200
|
131 |
+
}
|
chat_template.jinja
ADDED
@@ -0,0 +1,2 @@
|
|
|
|
|
|
|
1 |
+
<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>
|
2 |
+
{% endfor %}{% if add_generation_prompt %}{{ 'Assistant: ' }}{% endif %}
|
chat_template.json
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"chat_template": "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant: ' }}{% endif %}"
|
3 |
+
}
|
config.json
ADDED
@@ -0,0 +1,141 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
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|
|
|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
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|
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|
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|
|
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|
|
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|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"SmolVLMForConditionalGeneration"
|
4 |
+
],
|
5 |
+
"image_token_id": 49190,
|
6 |
+
"model_type": "smolvlm",
|
7 |
+
"pad_token_id": 128002,
|
8 |
+
"scale_factor": 4,
|
9 |
+
"text_config": {
|
10 |
+
"_flash_attn_2_enabled": true,
|
11 |
+
"_name_or_path": "None",
|
12 |
+
"architectures": [
|
13 |
+
"VLlama3ForCausalLM"
|
14 |
+
],
|
15 |
+
"head_dim": 64,
|
16 |
+
"hidden_size": 960,
|
17 |
+
"intermediate_size": 2560,
|
18 |
+
"is_llama_config": true,
|
19 |
+
"max_position_embeddings": 8192,
|
20 |
+
"model_type": "llama",
|
21 |
+
"neftune_noise_alpha": 0.0,
|
22 |
+
"num_attention_heads": 15,
|
23 |
+
"num_hidden_layers": 32,
|
24 |
+
"num_key_value_heads": 5,
|
25 |
+
"pad_token_id": 2,
|
26 |
+
"perceiver_config": {
|
27 |
+
"_attn_implementation_autoset": false,
|
28 |
+
"_name_or_path": "",
|
29 |
+
"add_cross_attention": false,
|
30 |
+
"architectures": null,
|
31 |
+
"attention_dropout": 0.0,
|
32 |
+
"bad_words_ids": null,
|
33 |
+
"begin_suppress_tokens": null,
|
34 |
+
"bos_token_id": null,
|
35 |
+
"chunk_size_feed_forward": 0,
|
36 |
+
"cross_attention_hidden_size": null,
|
37 |
+
"decoder_start_token_id": null,
|
38 |
+
"diversity_penalty": 0.0,
|
39 |
+
"do_sample": false,
|
40 |
+
"early_stopping": false,
|
41 |
+
"encoder_no_repeat_ngram_size": 0,
|
42 |
+
"eos_token_id": null,
|
43 |
+
"exponential_decay_length_penalty": null,
|
44 |
+
"finetuning_task": null,
|
45 |
+
"forced_bos_token_id": null,
|
46 |
+
"forced_eos_token_id": null,
|
47 |
+
"hidden_act": "silu",
|
48 |
+
"id2label": {
|
49 |
+
"0": "LABEL_0",
|
50 |
+
"1": "LABEL_1"
|
51 |
+
},
|
52 |
+
"is_decoder": false,
|
53 |
+
"is_encoder_decoder": false,
|
54 |
+
"label2id": {
|
55 |
+
"LABEL_0": 0,
|
56 |
+
"LABEL_1": 1
|
57 |
+
},
|
58 |
+
"length_penalty": 1.0,
|
59 |
+
"max_length": 20,
|
60 |
+
"min_length": 0,
|
61 |
+
"model_type": "vllama3",
|
62 |
+
"no_repeat_ngram_size": 0,
|
63 |
+
"num_beam_groups": 1,
|
64 |
+
"num_beams": 1,
|
65 |
+
"num_key_value_heads": 1,
|
66 |
+
"num_return_sequences": 1,
|
67 |
+
"output_attentions": false,
|
68 |
+
"output_hidden_states": false,
|
69 |
+
"output_scores": false,
|
70 |
+
"pad_token_id": null,
|
71 |
+
"prefix": null,
|
72 |
+
"problem_type": null,
|
73 |
+
"pruned_heads": {},
|
74 |
+
"qk_layer_norms_perceiver": false,
|
75 |
+
"remove_invalid_values": false,
|
76 |
+
"repetition_penalty": 1.0,
|
77 |
+
"resampler_depth": 6,
|
78 |
+
"resampler_head_dim": 96,
|
79 |
+
"resampler_n_heads": 16,
|
80 |
+
"resampler_n_latents": 64,
|
81 |
+
"return_dict": true,
|
82 |
+
"return_dict_in_generate": false,
|
83 |
+
"sep_token_id": null,
|
84 |
+
"suppress_tokens": null,
|
85 |
+
"task_specific_params": null,
|
86 |
+
"temperature": 1.0,
|
87 |
+
"tf_legacy_loss": false,
|
88 |
+
"tie_encoder_decoder": false,
|
89 |
+
"tie_word_embeddings": true,
|
90 |
+
"tokenizer_class": null,
|
91 |
+
"top_k": 50,
|
92 |
+
"top_p": 1.0,
|
93 |
+
"torch_dtype": null,
|
94 |
+
"torchscript": false,
|
95 |
+
"transformers_version": "4.46.0",
|
96 |
+
"typical_p": 1.0,
|
97 |
+
"use_bfloat16": false
|
98 |
+
},
|
99 |
+
"pixel_shuffle_factor": 4,
|
100 |
+
"qk_layer_norms": false,
|
101 |
+
"rms_norm_eps": 1e-05,
|
102 |
+
"rope_interleaved": false,
|
103 |
+
"rope_theta": 100000,
|
104 |
+
"torch_dtype": "bfloat16",
|
105 |
+
"transformers.js_config": {
|
106 |
+
"kv_cache_dtype": {
|
107 |
+
"fp16": "float16",
|
108 |
+
"q4f16": "float16"
|
109 |
+
}
|
110 |
+
},
|
111 |
+
"use_resampler": false,
|
112 |
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generation_config.json
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tokenizer.json
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tokenizer_config.json
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|
1 |
+
{
|
2 |
+
"add_prefix_space": false,
|
3 |
+
"added_tokens_decoder": {
|
4 |
+
"0": {
|
5 |
+
"content": "<|endoftext|>",
|
6 |
+
"lstrip": false,
|
7 |
+
"normalized": false,
|
8 |
+
"rstrip": false,
|
9 |
+
"single_word": false,
|
10 |
+
"special": true
|
11 |
+
},
|
12 |
+
"1": {
|
13 |
+
"content": "<|im_start|>",
|
14 |
+
"lstrip": false,
|
15 |
+
"normalized": false,
|
16 |
+
"rstrip": false,
|
17 |
+
"single_word": false,
|
18 |
+
"special": true
|
19 |
+
},
|
20 |
+
"2": {
|
21 |
+
"content": "<|im_end|>",
|
22 |
+
"lstrip": false,
|
23 |
+
"normalized": false,
|
24 |
+
"rstrip": false,
|
25 |
+
"single_word": false,
|
26 |
+
"special": true
|
27 |
+
},
|
28 |
+
"3": {
|
29 |
+
"content": "<repo_name>",
|
30 |
+
"lstrip": false,
|
31 |
+
"normalized": false,
|
32 |
+
"rstrip": false,
|
33 |
+
"single_word": false,
|
34 |
+
"special": true
|
35 |
+
},
|
36 |
+
"4": {
|
37 |
+
"content": "<reponame>",
|
38 |
+
"lstrip": false,
|
39 |
+
"normalized": false,
|
40 |
+
"rstrip": false,
|
41 |
+
"single_word": false,
|
42 |
+
"special": true
|
43 |
+
},
|
44 |
+
"5": {
|
45 |
+
"content": "<file_sep>",
|
46 |
+
"lstrip": false,
|
47 |
+
"normalized": false,
|
48 |
+
"rstrip": false,
|
49 |
+
"single_word": false,
|
50 |
+
"special": true
|
51 |
+
},
|
52 |
+
"6": {
|
53 |
+
"content": "<filename>",
|
54 |
+
"lstrip": false,
|
55 |
+
"normalized": false,
|
56 |
+
"rstrip": false,
|
57 |
+
"single_word": false,
|
58 |
+
"special": true
|
59 |
+
},
|
60 |
+
"7": {
|
61 |
+
"content": "<gh_stars>",
|
62 |
+
"lstrip": false,
|
63 |
+
"normalized": false,
|
64 |
+
"rstrip": false,
|
65 |
+
"single_word": false,
|
66 |
+
"special": true
|
67 |
+
},
|
68 |
+
"8": {
|
69 |
+
"content": "<issue_start>",
|
70 |
+
"lstrip": false,
|
71 |
+
"normalized": false,
|
72 |
+
"rstrip": false,
|
73 |
+
"single_word": false,
|
74 |
+
"special": true
|
75 |
+
},
|
76 |
+
"9": {
|
77 |
+
"content": "<issue_comment>",
|
78 |
+
"lstrip": false,
|
79 |
+
"normalized": false,
|
80 |
+
"rstrip": false,
|
81 |
+
"single_word": false,
|
82 |
+
"special": true
|
83 |
+
},
|
84 |
+
"10": {
|
85 |
+
"content": "<issue_closed>",
|
86 |
+
"lstrip": false,
|
87 |
+
"normalized": false,
|
88 |
+
"rstrip": false,
|
89 |
+
"single_word": false,
|
90 |
+
"special": true
|
91 |
+
},
|
92 |
+
"11": {
|
93 |
+
"content": "<jupyter_start>",
|
94 |
+
"lstrip": false,
|
95 |
+
"normalized": false,
|
96 |
+
"rstrip": false,
|
97 |
+
"single_word": false,
|
98 |
+
"special": true
|
99 |
+
},
|
100 |
+
"12": {
|
101 |
+
"content": "<jupyter_text>",
|
102 |
+
"lstrip": false,
|
103 |
+
"normalized": false,
|
104 |
+
"rstrip": false,
|
105 |
+
"single_word": false,
|
106 |
+
"special": true
|
107 |
+
},
|
108 |
+
"13": {
|
109 |
+
"content": "<jupyter_code>",
|
110 |
+
"lstrip": false,
|
111 |
+
"normalized": false,
|
112 |
+
"rstrip": false,
|
113 |
+
"single_word": false,
|
114 |
+
"special": true
|
115 |
+
},
|
116 |
+
"14": {
|
117 |
+
"content": "<jupyter_output>",
|
118 |
+
"lstrip": false,
|
119 |
+
"normalized": false,
|
120 |
+
"rstrip": false,
|
121 |
+
"single_word": false,
|
122 |
+
"special": true
|
123 |
+
},
|
124 |
+
"15": {
|
125 |
+
"content": "<jupyter_script>",
|
126 |
+
"lstrip": false,
|
127 |
+
"normalized": false,
|
128 |
+
"rstrip": false,
|
129 |
+
"single_word": false,
|
130 |
+
"special": true
|
131 |
+
},
|
132 |
+
"16": {
|
133 |
+
"content": "<empty_output>",
|
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1034 |
+
"special": true
|
1035 |
+
},
|
1036 |
+
"49264": {
|
1037 |
+
"content": "<|reserved_special_token_73|>",
|
1038 |
+
"lstrip": false,
|
1039 |
+
"normalized": false,
|
1040 |
+
"rstrip": false,
|
1041 |
+
"single_word": false,
|
1042 |
+
"special": true
|
1043 |
+
},
|
1044 |
+
"49265": {
|
1045 |
+
"content": "<|reserved_special_token_74|>",
|
1046 |
+
"lstrip": false,
|
1047 |
+
"normalized": false,
|
1048 |
+
"rstrip": false,
|
1049 |
+
"single_word": false,
|
1050 |
+
"special": true
|
1051 |
+
},
|
1052 |
+
"49266": {
|
1053 |
+
"content": "<|reserved_special_token_75|>",
|
1054 |
+
"lstrip": false,
|
1055 |
+
"normalized": false,
|
1056 |
+
"rstrip": false,
|
1057 |
+
"single_word": false,
|
1058 |
+
"special": true
|
1059 |
+
},
|
1060 |
+
"49267": {
|
1061 |
+
"content": "<|reserved_special_token_76|>",
|
1062 |
+
"lstrip": false,
|
1063 |
+
"normalized": false,
|
1064 |
+
"rstrip": false,
|
1065 |
+
"single_word": false,
|
1066 |
+
"special": true
|
1067 |
+
},
|
1068 |
+
"49268": {
|
1069 |
+
"content": "<|reserved_special_token_77|>",
|
1070 |
+
"lstrip": false,
|
1071 |
+
"normalized": false,
|
1072 |
+
"rstrip": false,
|
1073 |
+
"single_word": false,
|
1074 |
+
"special": true
|
1075 |
+
},
|
1076 |
+
"49269": {
|
1077 |
+
"content": "<|reserved_special_token_78|>",
|
1078 |
+
"lstrip": false,
|
1079 |
+
"normalized": false,
|
1080 |
+
"rstrip": false,
|
1081 |
+
"single_word": false,
|
1082 |
+
"special": true
|
1083 |
+
},
|
1084 |
+
"49270": {
|
1085 |
+
"content": "<|reserved_special_token_79|>",
|
1086 |
+
"lstrip": false,
|
1087 |
+
"normalized": false,
|
1088 |
+
"rstrip": false,
|
1089 |
+
"single_word": false,
|
1090 |
+
"special": true
|
1091 |
+
},
|
1092 |
+
"49271": {
|
1093 |
+
"content": "<|reserved_special_token_80|>",
|
1094 |
+
"lstrip": false,
|
1095 |
+
"normalized": false,
|
1096 |
+
"rstrip": false,
|
1097 |
+
"single_word": false,
|
1098 |
+
"special": true
|
1099 |
+
},
|
1100 |
+
"49272": {
|
1101 |
+
"content": "<|reserved_special_token_81|>",
|
1102 |
+
"lstrip": false,
|
1103 |
+
"normalized": false,
|
1104 |
+
"rstrip": false,
|
1105 |
+
"single_word": false,
|
1106 |
+
"special": true
|
1107 |
+
},
|
1108 |
+
"49273": {
|
1109 |
+
"content": "<|reserved_special_token_82|>",
|
1110 |
+
"lstrip": false,
|
1111 |
+
"normalized": false,
|
1112 |
+
"rstrip": false,
|
1113 |
+
"single_word": false,
|
1114 |
+
"special": true
|
1115 |
+
},
|
1116 |
+
"49274": {
|
1117 |
+
"content": "<|reserved_special_token_83|>",
|
1118 |
+
"lstrip": false,
|
1119 |
+
"normalized": false,
|
1120 |
+
"rstrip": false,
|
1121 |
+
"single_word": false,
|
1122 |
+
"special": true
|
1123 |
+
},
|
1124 |
+
"49275": {
|
1125 |
+
"content": "<|reserved_special_token_84|>",
|
1126 |
+
"lstrip": false,
|
1127 |
+
"normalized": false,
|
1128 |
+
"rstrip": false,
|
1129 |
+
"single_word": false,
|
1130 |
+
"special": true
|
1131 |
+
},
|
1132 |
+
"49276": {
|
1133 |
+
"content": "<|reserved_special_token_85|>",
|
1134 |
+
"lstrip": false,
|
1135 |
+
"normalized": false,
|
1136 |
+
"rstrip": false,
|
1137 |
+
"single_word": false,
|
1138 |
+
"special": true
|
1139 |
+
},
|
1140 |
+
"49277": {
|
1141 |
+
"content": "<|reserved_special_token_86|>",
|
1142 |
+
"lstrip": false,
|
1143 |
+
"normalized": false,
|
1144 |
+
"rstrip": false,
|
1145 |
+
"single_word": false,
|
1146 |
+
"special": true
|
1147 |
+
},
|
1148 |
+
"49278": {
|
1149 |
+
"content": "<|reserved_special_token_87|>",
|
1150 |
+
"lstrip": false,
|
1151 |
+
"normalized": false,
|
1152 |
+
"rstrip": false,
|
1153 |
+
"single_word": false,
|
1154 |
+
"special": true
|
1155 |
+
},
|
1156 |
+
"49279": {
|
1157 |
+
"content": "<end_of_utterance>",
|
1158 |
+
"lstrip": false,
|
1159 |
+
"normalized": false,
|
1160 |
+
"rstrip": false,
|
1161 |
+
"single_word": false,
|
1162 |
+
"special": true
|
1163 |
+
},
|
1164 |
+
"49280": {
|
1165 |
+
"content": "<action>",
|
1166 |
+
"lstrip": false,
|
1167 |
+
"normalized": false,
|
1168 |
+
"rstrip": false,
|
1169 |
+
"single_word": false,
|
1170 |
+
"special": true
|
1171 |
+
}
|
1172 |
+
},
|
1173 |
+
"additional_special_tokens": [
|
1174 |
+
"<action>"
|
1175 |
+
],
|
1176 |
+
"bos_token": "<|im_start|>",
|
1177 |
+
"clean_up_tokenization_spaces": false,
|
1178 |
+
"end_of_utterance_token": "<end_of_utterance>",
|
1179 |
+
"eos_token": "<end_of_utterance>",
|
1180 |
+
"extra_special_tokens": {
|
1181 |
+
"end_of_utterance_token": "<end_of_utterance>",
|
1182 |
+
"fake_image_token": "<fake_token_around_image>",
|
1183 |
+
"global_image_token": "<global-img>",
|
1184 |
+
"image_token": "<image>"
|
1185 |
+
},
|
1186 |
+
"fake_image_token": "<fake_token_around_image>",
|
1187 |
+
"global_image_token": "<global-img>",
|
1188 |
+
"image_token": "<image>",
|
1189 |
+
"legacy": false,
|
1190 |
+
"model_max_length": 8192,
|
1191 |
+
"pad_token": "<|im_end|>",
|
1192 |
+
"processor_class": "SmolVLMProcessor",
|
1193 |
+
"tokenizer_class": "GPT2Tokenizer",
|
1194 |
+
"truncation_side": "left",
|
1195 |
+
"unk_token": "<|endoftext|>",
|
1196 |
+
"vocab_size": 49152
|
1197 |
+
}
|
vocab.json
ADDED
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|
|