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README.md
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pipeline_tag: text-generation
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---
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ReidLM is a fine-tuned version of Meta's LLaMA 3 model, specifically optimized for generating high-quality, contextually accurate responses in the domain of rare diseases. <br>
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Utilizing the Evol-Instruct methodology, this model was fine-tuned with dataset of over 400 rare diseases.
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## Model Details
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### Model Description
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- **Developed by:** MSRIT
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- **Model type:** Transformer-based Large Language Model (LLM)
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- **Language(s) (NLP):** English
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- **License:**
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- **Finetuned from model:** Meta-Llama-3-8B-Instruct
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## Uses
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- **General Conversational AI:** While capable of generating detailed information on rare diseases, ReidLM may not be suitable for general conversational AI tasks that require a broad understanding of various topics. <br>
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## Bias, Risks, and Limitations
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ReidLM, like all large language models, has inherent biases and limitations that users should be aware of:<br>
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print(generated_text)
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```
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## Training Details
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#### Training Hyperparameters
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- **Training regime:**
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num_train_epochs=3, <br>
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per_device_train_batch_size=4,<br>
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gradient_accumulation_steps=2,<br>
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pipeline_tag: text-generation
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---
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## Model Details
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ReidLM is a fine-tuned version of Meta's LLaMA 3 model, specifically optimized for generating high-quality, contextually accurate responses in the domain of rare diseases. <br>
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Utilizing the Evol-Instruct methodology, this model was fine-tuned with dataset of over 400 rare diseases.
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### Model Description
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- **Developed by:** MSRIT
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- **Model type:** Transformer-based Large Language Model (LLM)
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- **Language(s) (NLP):** English
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- **License:**
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- **Finetuned from model:** Meta-Llama-3-8B-Instruct
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## Uses
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- **General Conversational AI:** While capable of generating detailed information on rare diseases, ReidLM may not be suitable for general conversational AI tasks that require a broad understanding of various topics. <br>
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## Bias, Risks, and Limitations
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ReidLM, like all large language models, has inherent biases and limitations that users should be aware of:<br>
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print(generated_text)
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```
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<br>
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## Training Details
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#### Training Hyperparameters
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num_train_epochs=3, <br>
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per_device_train_batch_size=4,<br>
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gradient_accumulation_steps=2,<br>
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