kimou605
/

BioTATA-7B

@@ -1,14 +1,21 @@
 ---
 license: apache-2.0
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
-BioTATA 7B is a hybrid model merged between BioMistral 7B Dare and a 4bit QLORA adapter trained on TATA/NO TATA sequences from INSTADEEP'S dataset
 ## Model Details
@@ -16,8 +23,6 @@ BioTATA 7B is a hybrid model merged between BioMistral 7B Dare and a 4bit QLORA
 <!-- 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:** Med Karim Akkari (kimou605)
 - **Funded by :** Med Karim Akkari (kimou605)
 - **Shared by :** Med Karim Akkari (kimou605)
@@ -31,10 +36,9 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 <!-- Provide the basic links for the model. -->
 - **Repository:** kimou605/BioTATA-7B
-- **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. -->
 ```python
@@ -124,18 +128,15 @@ This will inference the model on 4.8GB Vram
 This model has been developped to show how can a medical LLM adapt itself to identify sequences as TATA/NO TATA
 The adapter has been trained on a 53.3k rows for only 1 epoch (due to hardware limitations)
 ### 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
@@ -149,11 +150,6 @@ kimou605/TATA-NOTATA-FineMistral-nucleotide_transformer_downstream_tasks
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-Importing the dataset already preprocessed for mistral finetuning purposes
 #### Training Hyperparameters
 - **Training regime:** BF16 4bits <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
@@ -166,44 +162,6 @@ batch_per_gpu 32
 GPU: NVIDIA A40 45GB Vram
-## 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 -->
@@ -215,50 +173,8 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - **Cloud Provider:** vast.ai
 - **Compute Region:** Europe
-## 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 Card Contact
-[More Information Needed]

 ---
 license: apache-2.0
 library_name: transformers
+tags:
+- biology
+datasets:
+- kimou605/TATA-NOTATA-FineMistral-nucleotide_transformer_downstream_tasks
+- InstaDeepAI/nucleotide_transformer_downstream_tasks
+language:
+- en
+pipeline_tag: text-generation
 ---
 # Model Card for Model ID
 <!-- Provide a quick summary of what the model is/does. -->
+BioTATA 7B is a hybrid model merged between BioMistral 7B Dare and a 4bit QLORA adapter trained on TATA/NO TATA sequences from InstaDeepAI nucleotide_transformer_downstream_tasks dataset (promoters_all subset)
 ## Model Details
 <!-- Provide a longer summary of what this model is. -->
 - **Developed by:** Med Karim Akkari (kimou605)
 - **Funded by :** Med Karim Akkari (kimou605)
 - **Shared by :** Med Karim Akkari (kimou605)
 <!-- Provide the basic links for the model. -->
 - **Repository:** kimou605/BioTATA-7B
 - **Demo [optional]:** [More Information Needed]
+## How to Get Started with the Model
 <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 ```python
 This model has been developped to show how can a medical LLM adapt itself to identify sequences as TATA/NO TATA
 The adapter has been trained on a 53.3k rows for only 1 epoch (due to hardware limitations)
+THIS MODEL IS FOR RESEARCH PURPOSES NOT FOR PRODUCTION
 ### 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.
 ## Training Details
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 #### Training Hyperparameters
 - **Training regime:** BF16 4bits <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 GPU: NVIDIA A40 45GB Vram
 ## Environmental Impact
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 - **Cloud Provider:** vast.ai
 - **Compute Region:** Europe
 ## Model Card Contact
+Karim Akkari (kimou605)