nymtheescobar
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update readme
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README.md
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license: llama3.1
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base_model:
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datasets:
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- BanglaLLM/bangla-alpaca-orca
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tags:
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## Model description
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The Bangla LLaMA models have been enhanced and tailored specifically with an extensive Bangla vocabulary of 16,000 tokens, building upon the foundation set by the original LLaMA-3.1
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- **Model type:** A 8B parameter model for Causal LM instruct finetuned with BanglaLLM/bangla-alpaca-orca.
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- **Language(s):** Bangla and English
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- **License:** GNU General Public License v3.0
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| Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) |
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| Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) |
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| Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) |
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| Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](BanglaLLM/bangla-llama-13b-instruct-v0.1) |
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## Usage Note
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It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
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- bn
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- en
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license: llama3.1
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base_model:
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- BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1
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datasets:
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- BanglaLLM/bangla-alpaca-orca
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tags:
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## Model description
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- **Model type:** A 8B parameter model for Causal LM instruct finetuned with BanglaLLM/bangla-alpaca-orca.
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- **Language(s):** Bangla and English
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- **License:** GNU General Public License v3.0
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| Bangla LLaMA 7B Base | Base model | 12GB | LLaMA 7B | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-base-v0.1) |
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| Bangla LLaMA 13B Base | Base model | 4GB | LLaMA 13B | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-base-v0.1) |
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| Bangla LLaMA 7B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 7B Base | 7B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-7b-instruct-v0.1) |
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| Bangla LLaMA 13B Instruct | Instruction following model | 145k instructions | Bangla LLaMA 13B Base | 13B | [HF Hub](https://huggingface.co/BanglaLLM/bangla-llama-13b-instruct-v0.1) |
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| Bangla LLaMA 3 8B Base | Base model | 12.4M | LLaMA 3 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-unolp-culturax-base-v0.0.1)
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| Bangla LLaMA 3 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3 8B Base | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3-8b-bangla-alpaca-orca-instruct-v0.0.1)
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| Bangla LLaMA 3.1 8B Base | Base model | 12.4M | LLaMA 3.1 8b | 8B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-unolp-culturax-base-v0.0.1)
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| Bangla LLaMA 3.1 8B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.1 8B Base | 8b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.1-8b-bangla-alpaca-orca-instruct-v0.0.1)
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| Bangla LLaMA 3.2 1B Base | Base model | 12.4M | LLaMA 3.2 1b | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-unolp-culturax-base-v0.0.1)
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| Bangla LLaMA 3.2 1B Instruct | Instruction following model | 172k instructions | Bangla LLaMA 3.2 1B Base | 1b | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-1b-bangla-alpaca-orca-instruct-v0.0.1)
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| Bangla LLaMA 3.2 3B Instruct| Instruction following model | 172k instructions | Bangla LLaMA 3.2 3B Base | 3B | [HF Hub](https://huggingface.co/BanglaLLM/BanglaLLama-3.2-3b-bangla-alpaca-orca-instruct-v0.0.1)
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## Usage Note
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It's important to note that the models have not undergone detoxification. Therefore, while they possess impressive linguistic capabilities, there is a possibility for them to generate content that could be deemed harmful or offensive. We urge users to exercise discretion and supervise the model's outputs closely, especially in public or sensitive applications.
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