kuleshov-group
/

mdlm-owt

@@ -7,175 +7,32 @@ datasets:
   - Skylion007/openwebtext
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-This is a masked diffusion model that generates text using a diffusion process trained on the OpenWebText dataset.
 ## 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:** Masked Language Model
-- **Language(s) (NLP):** en
-- **License:** Apache 2.0
-### Model Sources
-<!-- Provide the basic links for the model. -->
-- **Repository:** https://github.com/kuleshov-group/mdlm
-- **Paper [optional]:** https://arxiv.org/abs/2406.07524
-- **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. -->
-* Research
-### 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 -->
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-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-### 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. -->
-https://huggingface.co/datasets/Skylion007/openwebtext
-### 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
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-[More Information Needed]
-#### Factors
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-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-### Results
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-#### 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
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-#### Hardware
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-#### Software
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 ## 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:**
 ```
@@ -202,20 +59,5 @@ year = {2024}
 }
 ```
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
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 ## Model Card Contact
-[More Information Needed]

   - Skylion007/openwebtext
 ---
+## Using MDLM
+To use the pre-trained model for masked language modeling, use the following snippet:
+```python
+from transformers import AutoModelForMaskedLM, AutoTokenizer
+# See the `Caduceus` collection page on the hub for list of available models.
+tokenizer = transformers.AutoTokenizer.from_pretrained('gpt2')
+model_name = 'kuleshov-group/mdlm-no_flashattn-fp32-owt'
+model = AutoModelForMaskedLM.from_pretrained(model_name)
+```
 ## Model Details
+The model, which has a context length of `1024` and is similar in size to GPT2-medium with approximately `130 million` non-embedding parameters,
+was trained using a forward diffusion process that generates inputs varying from fully masked to fully unmasked. Its objective is to
+reconstruct the original input from these varying levels of masking, outputting logits in the process.
+The training regimen comprised one million steps on the OpenWebText corpus, involving the processing of a total of `33 billion` tokens.
+For more details, please see our paper: [Simple and Effective Masked Diffusion Language Models](http://arxiv.org/abs/2406.07524).
 ## 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. -->
+Please cite our work using the bibtex below:
 **BibTeX:**
 ```
 }
 ```
 ## Model Card Contact
+Subham Sekhar Sahoo (ssahoo@cs.cornell.edu)