Question Answering
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Safetensors
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trl
sft
unsloth
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BongLlama3 / README.md
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---
language:
- bn
- en
license: apache-2.0
library_name: transformers
tags:
- trl
- sft
- unsloth
datasets:
- lumatic-ai/BongChat-v1-253k
- BanglaLLM/bangla-alpaca-orca
- ahnaf702/Alpaca_orca_bongchat_merged
metrics:
- accuracy
pipeline_tag: question-answering
---
# Model Card for Model ID
Introducing BongLlama3 . A finetuned version of Llama3 8B Chat on Bengali Dataset.
## Model Details
Llama3 8B shrank to 0.34B by implementing QLORA, Bongllama3 is a LLM built on Bengali dataset. It's a 0.34B parameters model. We have used a Merged Bengali dataset(Alpaca_Orca+Bongchat) of 643k data and finetuned on LLAMA3 8b model to get our Bongllama model.
We are continuously working on training and developing this model and improve it. We are also going to launch this model with various sizes of different LLM's and Datasets.
### Model Description
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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:** Ahnaf
- **Shared by [Optional]:** Ahnaf
- **Model type:** Language model
- **Language(s) (NLP):** en, bn
- **License:** mit
- **Parent Model:** meta-llama/Meta-Llama-3-8B
### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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
- **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]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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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]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Software
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## Citation [optional]
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**APA:**
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## Glossary [optional]
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