BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:32kTool Calling:SupportedPublished:Sep 19, 2024License:llama3.1Architecture:Transformer0.0K Cold

BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1 is an 8 billion parameter instruction-tuned causal language model developed by BanglaLLM, based on LLaMA 3.1. This model is specifically fine-tuned using the Bangla-Alpaca-Orca dataset, making it highly proficient in generating and understanding Bangla language instructions. Its primary use case is for advanced natural language processing tasks in Bangla, offering strong linguistic capabilities for applications requiring instruction following in both Bangla and English.

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Overview

BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1 is an 8 billion parameter instruction-tuned causal language model, developed by BanglaLLM. It is built upon the LLaMA 3.1 8B base model and has been fine-tuned using the Bangla-Alpaca-Orca dataset, making it a significant advancement for Bangla language LLMs. The model supports both Bangla and English languages and is designed for immediate inference.

Key Capabilities

  • Bangla Instruction Following: Specifically fine-tuned for understanding and generating responses based on instructions in Bangla.
  • Causal Language Modeling: Functions as a foundational model for causal language modeling tasks.
  • Dual Language Support: Capable of processing and generating text in both Bangla and English.
  • LLaMA 3.1 Architecture: Leverages the robust LLaMA 3.1 8B architecture for strong performance.

Good for

  • Bangla NLP Applications: Ideal for developing applications that require advanced natural language understanding and generation in Bangla.
  • Instruction-Based Tasks: Suitable for tasks where the model needs to follow specific instructions, such as question answering, summarization, or content creation in Bangla.
  • Research and Development: Valuable for researchers exploring LLM capabilities and advancements in low-resource languages like Bangla.

Note: The model has not undergone detoxification and may generate harmful or offensive content. Users should exercise discretion and supervise outputs, especially in public or sensitive applications.

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