Update README.md

README.md

@@ -7,35 +7,31 @@ base_model:
 - mistralai/Pixtral-12B-2409
 - mistral-community/pixtral-12b
 ---
-# Model Card for Model ID
+# Model Card for Moshika Vision 
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
 
 ## Model Details
 
 ### Model Description
 
 <!-- Provide a longer summary of what this model is. -->
+MoshiVis is a perceptually augmented version of Moshi, giving it the ability to freely discuss images whilst maintaining its natural conversation style and low latency.
+To achieve this, Moshi has been extended with a visual backbone and a cross-attention mechanism to infuse the visual information into the language model.
 
-
-
-- **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]
+- **Developed by:** Kyutai
+- **Model type:** Multimodal speech+vision+text foundation model
+- **Language(s) (NLP):** English
+- **License:** Apache License 2.0
+- **Finetuned from model:** [Moshika](https://huggingface.co/kyutai/moshika-vis-pytorch-bf16) and [Pixtral](https://huggingface.co/mistral-community/pixtral-12b)
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:** [Github kyutai-labs/moshivis](https://github.com/kyutai-labs/moshivis) <-- TODO: Update / check link
+- **Demo [optional]:** [moshi.chat](https://moshi.chat/)
 
 ## Uses
 
@@ -44,160 +40,60 @@ This modelcard aims to be a base template for new models. It has been generated
 ### Direct Use
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+Similar to Moshi itself, MoshiVis can be used as a conversational agent for casual conversations, basic facts and advice (e.g. recipes, trivia), roleplay, etc. 
+In addition, MoshiVis is able to recognize and discuss images in a natural way, whilst still allowing for low-latency interactions.
 
-[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]
+The model is not intended to be used to impersonate other people or any malicious use of any kind. 
+This model is for research only and we do not recommend it for providing advices or to perform any professionnal duty.
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+MoshiVis has been designed to perceptually augment the original Moshi model with vision capabilities and is expected to inherit similar biases and limitations, see also [Moshika](https://huggingface.co/kyutai/moshika-vis-pytorch-bf16).
+Our analysis with respect to how much MoshiVis diverges from the original model is still ongoing.
 
-[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]
+See the [README file](https://github.com/kyutai-labs/moshivis) for getting started.  <-- TODO: Update / check link
 
 ## 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. -->
+### Model Architecture and Objective
+Our goal was to design an efficient and effective adaptation mechanism that allows Moshi to discuss images whilst maintaining its previous conversational capabilities.
+To achieve this, we train a cross-attention mechanism to insert image information from a pretrained and frozen vision backbone into the underlying language model, which is also kept frozen.
+An additional gating mechanism ensures that the insertion of visual information does not impact the interaction with Moshi outside of discussions of images, allowing for a seamless back and forth between general and image-specific conversations.
 
-[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. -->
+Stay tuned for our technical report, in which we will describe the training procedure in detail!
 
-#### 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]
+### Training Data
 
-## Technical Specifications [optional]
+<!-- 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. -->
+For information on the training data used for the base models, see [Pixtral](https://mistral.ai/news/pixtral-12b/) and [Moshi](https://huggingface.co/kyutai/moshika-pytorch-bf16) respectively.
+To train the cross-attention and gating mechanism that MoshiVis uses for processing images, we rely on a collection of publicly available datasets:
+- [Pixelprose](https://arxiv.org/abs/2406.10328)
+- [DOCCI](https://arxiv.org/abs/2404.19753)
+- [TallyQA](https://arxiv.org/abs/1810.12440)
+- [OCRVQA](https://ocr-vqa.github.io/)
+- [RenderedText](https://huggingface.co/datasets/wendlerc/RenderedText)
+- [DocVQA](https://arxiv.org/abs/2007.00398)
+- [ChartQA](https://aclanthology.org/2022.findings-acl.177/)
 
-### Model Architecture and Objective
+We will share additional details soon, stay tuned!
 
-[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]
+MoshiVis was designed as a relatively low-cost adaptation of Moshi and was trained on a single DGX node with 8 H100 GPUs provided by Scaleway.
 
-## Model Card Contact
+## Model Card Authors
 
-[More Information Needed]
+Amélie Royer, Moritz Böhle