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Haaaaarsh commited on Nov 19, 2024

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@@ -1,45 +1,30 @@
 ---
 library_name: transformers
 tags:
-- llama-factory
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## 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:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **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
-### Use with transformers
 Make sure to update your transformers installation via `pip install --upgrade transformers`.
 ```python
 import torch
 from transformers import pipeline
@@ -62,106 +47,22 @@ outputs = pipe(
 print(outputs[0]["generated_text"][-1])
 ```
-### 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 -->
-[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]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[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]
 ## 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. -->
-[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. -->
-#### 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
@@ -170,52 +71,4 @@ Use the code below to get started with the model.
 - **Cloud Provider:** Google Cloud Platform
 - **Compute Region:** asia-southeast1
 - **Carbon Emitted:** Total emissions are estimated to be 0.85 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### 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:**
-[More Information Needed]
-**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. -->
-[More Information Needed]
-## 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
+pipeline_tag: text-generation
 tags:
+- BGPT
+- meta
+- pytorch
+- llama
+- llama-3
 ---
 ### Model Description
+This model is a finetuned version of Llama3.2-3B-Instruct specifically designed for generating multilingual outputs across multiple Indic languages. The model has been trained on a diverse and curated dataset comprising Hindi, Punjabi, Marathi, Malayalam, Oriya, Kannada, Gujarati, Bengali, Urdu, Tamil, and Telugu. It is optimized to handle natural language tasks such as translation, summarization, and conversational generation across these languages effectively.
 - **Developed by:** [More Information Needed]
+- **Model type:** Finetuned LLaMA (Language Model for Multilingual Text Generation)
+- **Language(s) (NLP):** Hindi, Punjabi, Marathi, Malayalam, Oriya, Kannada, Gujarati, Bengali, Urdu, Tamil, Telugu
+- **Finetuned from model:** Llama3.2-3B-Instruct
+## How to Get Started with the Model
 Make sure to update your transformers installation via `pip install --upgrade transformers`.
+Use the code below to get started with the model.
 ```python
 import torch
 from transformers import pipeline
 print(outputs[0]["generated_text"][-1])
 ```
 ## Training Details
 ### Training Data
+The training dataset included a diverse collection of text sources in:
+- Hindi, Punjabi, Marathi, Malayalam, Oriya, Kannada, Gujarati, Bengali, Urdu, Tamil, and Telugu.
+### Training Parameters
+- **Optimization Technique**: LoRA (Low-Rank Adaptation)
+- **Epochs**: 3.0
+- **Batch Size**: 2.0 (per device train batch size)
+- **Learning Rate**: 5e-05
 ## Environmental Impact
 - **Cloud Provider:** Google Cloud Platform
 - **Compute Region:** asia-southeast1
 - **Carbon Emitted:** Total emissions are estimated to be 0.85 kgCO$_2$eq of which 100 percents were directly offset by the cloud provider.