Update README.md

README.md

@@ -1,199 +1,217 @@
 ---
 library_name: transformers
-tags: []
+license: apache-2.0
+datasets:
+- HuggingFaceM4/the_cauldron
+- HuggingFaceM4/Docmatix
+- lmms-lab/LLaVA-OneVision-Data
+- lmms-lab/M4-Instruct-Data
+- HuggingFaceFV/finevideo
+- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
+- lmms-lab/LLaVA-Video-178K
+- orrzohar/Video-STaR
+- Mutonix/Vript
+- TIGER-Lab/VISTA-400K
+- Enxin/MovieChat-1K_train
+- ShareGPT4Video/ShareGPT4Video
+pipeline_tag: video-text-to-text
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolVLM-Instruct
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM2_banner.png" width="800" height="auto" alt="Image description">
 
+# SmolVLM2 2.2B
 
+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.
+## Model Summary
 
-## Model Details
+- **Developed by:** Hugging Face 🤗
+- **Model type:** Multi-modal model (image/multi-image/video/text)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Architecture:** Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary)
 
-### Model Description
+## Resources
 
-<!-- Provide a longer summary of what this model is. -->
+- **Demo:** [Video Highlight Generator](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator)
+- **Blog:** [Blog post](https://huggingface.co/blog/smolvlm2)
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
 
-[More Information Needed]
+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.
+
+To fine-tune SmolVLM2 on a specific task, you can follow [the fine-tuning tutorial](UPDATE).
+
+## Evaluation 
+
+### Vision Evaluation
 
-## Model Card Authors [optional]
+| Size  | Mathvista | MMM U | OCRBench | MMStar | AI2D | ChartQA_Test | Science_QA | TextVQA Val | DocVQA Val |
+|-------|----------|-------|----------|--------|------|--------------|------------|-------------|------------|
+| 2.2B  | 51.5     | 42    | 72.9     | 46     | 70   | 68.84        | 90         | 73.21       | 79.98      |
 
-[More Information Needed]
 
-## Model Card Contact
+### Video Evaluation
+We evaluated the performance of the SmolVLM2 family on the following scientific benchmarks:
 
-[More Information Needed]
+| Size    | Video-MME | MLVU | MVBench |
+|----------|-----------------|----------|---------------|
+| 2.2B   | 52.1            | 55.2     | 46.27        |
+| 500M | 42.2            | 47.3     | 39.73        |
+| 256M | 33.7            | 40.6     | 32.7          |
+
+
+### How to get started
+
+You can use transformers to load, infer and fine-tune SmolVLM. Make sure you have num2words, flash-attn and latest transformers installed.
+You can load the model as follows.
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+
+model_path = "HuggingFaceTB/SmolVLM2-2.2B-Instruct"
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2"
+).to("cuda")
+```
+
+#### Simple Inference
+
+You preprocess your inputs directly using chat templates and directly passing them 
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+{"type": "text", "text": "What is in this image?"},
+            {"type": "image", "path": "path_to_img.png"},
+            
+        ]
+    },
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+print(generated_texts[0])
+```
+
+#### Video Inference
+
+To use SmolVLM2 for video inference, make sure you have decord installed. 
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "path": "path_to_video.mp4"},
+            {"type": "text", "text": "Describe this video in detail"}
+        ]
+    },
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+
+print(generated_texts[0])
+```
+#### Multi-image Interleaved Inference
+
+You can interleave multiple media with text using chat templates.
+
+```python
+import torch
+
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+{"type": "text", "text": "What is the similarity between this image <image>"},
+
+            {"type": "image", "path": "image_1.png"},
+{"type": "text", "text": "and this image <image>"},
+{"type": "image", "path": "image_2.png"},            
+        ]
+    },
+]
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+print(generated_texts[0])
+```
+
+
+### Model optimizations
+
+## Misuse and Out-of-scope Use
+
+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:
+
+- Prohibited Uses:
+  - Evaluating or scoring individuals (e.g., in employment, education, credit)
+  - Critical automated decision-making
+  - Generating unreliable factual content
+- Malicious Activities:
+  - Spam generation
+  - Disinformation campaigns
+  - Harassment or abuse
+  - Unauthorized surveillance
+
+### License
+
+SmolVLM2 is built upon [the shape-optimized SigLIP](https://huggingface.co/google/siglip-so400m-patch14-384) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) for text decoder part.
+
+We release the SmolVLM2 checkpoints under the Apache 2.0 license.
+
+## Training Data
+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).
+In the following plots we give a general overview of the samples across modalities and the source of those samples.
+
+<center><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_data_split.png" width="auto" height="auto" alt="Image description">
+</center>
+
+### Details
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_datadetails.png" width="auto" height="auto" alt="Image description">