---

library_name: transformers
license: mit
language: bn
base_model: meta-llama/Llama-3.1-8B-Instruct

---

# Abegi-Llama3

## Model Card Summary

**Abegi-Llama3** is a Bangla-focused large language model fine-tuned from **Meta LLaMA‑3.1‑8B‑Instruct**. The model is optimized for Bangla (bn) conversational text generation and instruction-following tasks, while retaining general-purpose reasoning and generation capabilities inherited from the base model.

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## Model Details

### Model Description

Abegi-Llama3 is a decoder-only Transformer-based causal language model fine-tuned to improve naturalness, fluency, and instruction-following behavior in Bangla. It is suitable for chat-style interactions, content generation, and educational or research use cases involving the Bangla language.

* **Developed by:** Promit123546
* **Model type:** Causal Language Model (Decoder-only Transformer)
* **Base model:** meta-llama/Llama-3.1-8B-Instruct
* **Language(s):** Bangla (bn), with partial English support inherited from the base model
* **License:** LLaMA 3 License (inherited from base model)
* **Fine-tuned from:** meta-llama/Llama-3.1-8B-Instruct

### Model Sources

* **Repository:** [https://huggingface.co/Promit123546/Abegi-Llama3](https://huggingface.co/Promit123546/Abegi-Llama3)
* **Base Model:** [https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)

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## Uses

### Direct Use

The model can be used directly for:

* Bangla conversational agents and chatbots
* Bangla text generation and rewriting
* Question answering in Bangla
* Educational and experimental NLP applications

### Downstream Use

With further fine-tuning, the model can be adapted for:

* Domain-specific Bangla assistants (education, customer support, documentation)
* Bangla instruction-following systems
* Research on low-resource or regional language modeling

### Out-of-Scope Use

The model is **not recommended** for:

* Medical, legal, or financial decision-making
* High-stakes or safety-critical systems
* Generating harmful, misleading, or malicious content

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## Bias, Risks, and Limitations

* The model may reflect biases present in the training and fine-tuning data
* It can produce hallucinated or incorrect information
* Performance may degrade for tasks outside Bangla or conversational generation
* Cultural or linguistic nuances may not always be handled perfectly

### Recommendations

* Verify critical outputs using trusted external sources
* Apply moderation and safety filters in production environments
* Avoid use in sensitive or high-risk applications without human oversight

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## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "Promit123546/Abegi-Llama3"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "বাংলায় কৃত্রিম বুদ্ধিমত্তা কী?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=150)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

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## Training Details

### Training Data

* **Description:** Not publicly disclosed
* **Notes:** The model was fine-tuned on curated Bangla and instruction-style text data suitable for conversational generation

### Training Procedure

#### Preprocessing

* Tokenization using the LLaMA‑3.1 tokenizer
* Standard text normalization and prompt–response formatting

#### Training Hyperparameters

* **Training regime:** Mixed precision (fp16 or bf16)

#### Speeds, Sizes, Times

* **Checkpoint size:** ~8B parameters (base model)
* **Training duration:** Not publicly disclosed

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## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data

* Internal and informal Bangla prompt-based evaluation

#### Factors

* Fluency in Bangla
* Instruction adherence
* Coherence and relevance

#### Metrics

* Qualitative human evaluation

### Results

The model demonstrates fluent Bangla text generation and stable conversational behavior. No formal benchmark results are currently published.

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## Model Examination

No formal interpretability or probing studies have been conducted.

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## Environmental Impact

Environmental impact metrics were not recorded during training.

Carbon emissions may be estimated using the Machine Learning Impact Calculator (Lacoste et al., 2019) if compute details become available.

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## Technical Specifications

### Model Architecture and Objective

* Decoder-only Transformer architecture
* Auto-regressive next-token prediction objective

### Compute Infrastructure

#### Hardware

* Not publicly disclosed

#### Software

* Python
* PyTorch
* Hugging Face Transformers

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## Citation

If you use this model, please cite the base LLaMA‑3.1 model and this repository.

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## Model Card Authors

* Promit123546

## Model Card Contact

For questions or issues, please use the Hugging Face model page discussion section.