Update README

README.md

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-library_name: transformers
-tags: []
+library_name: peft
+license: apache-2.0
+base_model:
+- Qwen/Qwen3-VL-4B-Instruct
+pipeline_tag: visual-document-retrieval
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Eager Embed V1
 
+**eager-embed-v1** is a multimodal dense embedding model built upon a Vision-Language Model (VLM). It is designed to efficiently index documents using both their visual and textual features.
 
+Compared to multi-vector (ColBERT-like) architectures, eager-embed-v1 offers a strong balance between embedding dimensionality and retrieval accuracy, while maintaining efficiency. Unlike those approaches, it does not require a max-sim distance function, further simplifying the retrieval process.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-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. -->
+- **Developed by:** Juan Pablo Balarini
+- **Funded by:** [Eagerworks](https://eagerworks.com/)
+- **Model type:** Embedding model
+- **License:** Apache 2.0
+- **Finetuned from model:** Qwen3-VL-4B-Instruct
 
-[More Information Needed]
+### Model Sources
 
-### 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.
+- **Repository:** [eager-embed](https://github.com/eagerworks/eager-embed)
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
+**TODO: Coming soon**. Check [here for now](https://github.com/eagerworks/eager-embed/blob/main/inference.py) for now.
+```python
+TODO: Add inference code
+```
 
 ## 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]
+Trained was done using the following machine:
+- 8x RTX 5090 for a total of 256 GB of VRAM
+- AMD EPYC 9534 64-Core CPU (128 threads)
+- 256 RAM
+- 2TB SSD
 
 ### 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 was done using the [Tevatron framework](https://github.com/texttron/tevatron) and [Deepspeed](https://github.com/deepspeedai/DeepSpeed) for parallel training.
 
 #### 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]
+More information on training parameters can be found [here](https://github.com/eagerworks/eager-embed/blob/main/train.sh)
 
 ## 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]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+Model was evaluated on the Vidore 1, 2 and 3 benchmarks. More info can be found [here](https://mteb-leaderboard.hf.space/?benchmark_name=ViDoRe%28v2%29).
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
+```bibtex
+@article{EagerEmbed,
+  title={Eager Embed V1: Multimodal Dense Embeddings for Retrieval},
+  author={Juan Pablo Balarini},
+  year={2025},
+  publisher={Eagerworks},
+  url={https://github.com/eagerworks/eager-embed}
+}
+```