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  pipeline_tag: text-generation
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  ---
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- # Model Card for ReidLM
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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:** [More Information Needed]
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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>