modularStarEncoder
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ModularStarEncoder

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ModularStarEncoder

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andreagurioli1995 commited on Feb 21

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@@ -1,27 +1,31 @@
 ---
 library_name: transformers
 datasets:
-- bigcode/the-stack-v2
 license: bigcode-openrail-m
 ---
-# Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-You can download the tokenizer following:
-from transformers import AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder")
-Input should take this format when tokenized:
-f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}"
 ### How to use
 ```python
@@ -39,7 +43,7 @@ tokenizer = AutoTokenizer.from_pretrained("andreagurioli1995/ModularStarEncoder"
 code_snippet = "your code to embed here"
 #You should follow this pattern to embed a snippet of code
-sentence =  f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}
 #Tokenizing your sentence
 tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
@@ -57,189 +61,23 @@ You will get as an output six elements:
 - seq_relationship_scores: prediction scores of in-context loss (concatenate multiple samples with the separator token if you want a meaningful score)
 - attentions: attention scores from the encoder
-### 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]
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-- **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 -->
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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
-<!-- This section is meant to convey both technical and sociotechnical 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. -->
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-### 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
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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:**
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-**APA:**
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-## 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
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 ---
 library_name: transformers
 datasets:
+- bigcode/the-stack-v2-train-full-ids
 license: bigcode-openrail-m
 ---
+# ModularStarEncoder-1B Pre-trained model
 <!-- Provide a quick summary of what the model is/does. -->
+ModularStarEncoder-1B is an encoder pre-trained on [The Stack v2](https://huggingface.co/datasets/bigcode/the-stack-v2-train).
+ModularStarEncoder is a modular pre-trained encoder with five exit points, allowing users to perform multiple exit fine-tuning depending on downstream tasks.
+We built ModularStarEncoder on top of [StarCoder-2](https://huggingface.co/bigcode/starcoder2-15b), reducing its size from 15B to 1B parameters in bfloat16.
+Our architecture consists of 36 hidden layers, each with 16 attention heads and 4 key-value heads, using Grouped Query Attention (GQA).
+The model employs Rotary Positional Encoding (RoPE) with a base period theta = 10^-6 and features a hidden dimensionality of 1024 with an intermediate size of 12,288.
+To enhance efficiency, we replaced the causal self-attention layers with bidirectional self-attention. Unlike [StarCoder-2](https://huggingface.co/bigcode/starcoder2-15b), which uses sliding window attention, we opted for full attention to ensure greater modularity, avoiding the receptive field constraints of sliding window mechanisms. Additionally, we extended the maximum input length to 2048 tokens, accommodating longer code snippets compared to prior code encoders such as [StarEncoder](https://huggingface.co/bigcode/starencoder).
+Finally, our implementation integrates FlashAttention V2 for faster inference.
+- **Paper:** [Link](arxiv.paper)
+- **Languages:** 600+ Programming languages
 ### How to use
 ```python
 code_snippet = "your code to embed here"
 #You should follow this pattern to embed a snippet of code
+sentence =  f"{tokenizer.sep_token}{code_snippet}{tokenizer.cls_token}"
 #Tokenizing your sentence
 tokenized_sensence = tokenizer(sentence, return_tensors="pt",truncation=True, max_length=2048)
 - seq_relationship_scores: prediction scores of in-context loss (concatenate multiple samples with the separator token if you want a meaningful score)
 - attentions: attention scores from the encoder
+### Training
 <!-- Provide a longer summary of what this model is. -->
+We pre-trained ModularStarEncoder with a batch size of 3.99M tokens for 245,000 training steps, processing 1T tokens.
+The pre-training and fine-tuning were conducted on 512 NVIDIA Ampere (64GB) GPUs using the [Leonardo](https://arxiv.org/abs/2307.16885) supercomputer, requiring 450,000 GPU working hours.
+| Hyperparameter           | Value     |
+|--------------------------|-----------|
+| Hidden size              | 1024      |
+| Max. position embeddings | 2048      |
+| Num. of attention heads  | 12        |
+| Num. of key values heads | 4         |
+| Num. of hidden layers    | 36        |
+| Attention                | GQA       |
+| Num. of parameters       | ≈1B       |
+| Training tokens          | ≈1T       |
+|Loss function             |MLM + In-Context loss|
+## Licence
+The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).