modularStarEncoder
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ModularStarEncoder-finetuned

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ModularStarEncoder

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andreagurioli1995 commited on Feb 21

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@@ -3,17 +3,22 @@ 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
 ### How to use
 ```python
@@ -53,189 +58,24 @@ You will get as an output three elements:
 - raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
 - 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]
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-## Uses
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-### Direct Use
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-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.
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-## Training Details
-### Training Data
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Results
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-#### Summary
-## Model Examination [optional]
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-## Environmental Impact
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-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).
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-## Technical Specifications [optional]
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 datasets:
 - bigcode/the-stack-v2
 license: bigcode-openrail-m
+base_model:
+- andreagurioli1995/ModularStarEncoder
 ---
+# ModularStarEncoder-1B Fine-Tuned model
 <!-- Provide a quick summary of what the model is/does. -->
+ModularStarEncoder-finetuned is an encoder built on top of [ModularStarEncoder-1B Pre-trained](https://huggingface.co/andreagurioli1995/ModularStarEncoder) on [SynthCode2Code2NL](add link here).
+ModularStarEncoder fine-tuned encoder for various retrieval tasks, enabling the end user to select the model size that meets their memory and computational constraints.
+We built ModularStarEncoder on top of [StarCoder-2](https://huggingface.co/bigcode/starcoder2-15b), reducing its size from 15B to 1B parameters in bfloat16.
+The model is finetuned with [CLIP objective](https://github.com/mlfoundations/open_clip/blob/main/src/open_clip/loss.py)
+- **Paper:** [Link](arxiv.paper)
+- **Languages:** English, Go, Ruby, Python, Java, C++, PHP, C, JavaScript
 ### How to use
 ```python
 - raw_hidden_states: raw representation from all the hidden states of the model, without pooling, normalization, and projection
 - attentions: attention scores from the encoder
+### Training
 <!-- Provide a longer summary of what this model is. -->
+We fine-tuned ModularStarEncoder with a batch size of 2048 contrastive samples for 20,000 training steps.
+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       |
+|Loss function             |CLIP loss  |
+|Multi-layer loss          | yes       |
+## 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).