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# Model Card for ReidLM
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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.
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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 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
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## Uses
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ReidLM is specifically designed for generating information related to rare diseases and should not be used for the following purposes:
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-Non-Medical Domains: ReidLM is optimized for rare disease information and may not perform well in other domains such as finance, law, general health conditions, or any other non-medical fields
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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.
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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
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-Ethical Concerns: There is a risk of over-reliance on AI for medical decisions, which should always be validated by healthcare professionals
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-Accuracy: While the model strives for accuracy, it may generate incorrect or incomplete information, especially in highly specialized or novel cases.
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<!---### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.--->
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##
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Use the code below to get started with the model.
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## Use with Transformers AutoModelForCausalLM
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#### Training Hyperparameters
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- **Training regime:**
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<!---#### Speeds, Sizes, Times [optional]
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# Model Card for ReidLM
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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 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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ReidLM is specifically designed for generating information related to rare diseases and should not be used for the following purposes:
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-Non-Medical Domains: ReidLM is optimized for rare disease information and may not perform well in other domains such as finance, law, general health conditions, or any other non-medical fields.<br>
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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.
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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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-Ethical Concerns: There is a risk of over-reliance on AI for medical decisions, which should always be validated by healthcare professionals.<br>
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-Accuracy: While the model strives for accuracy, it may generate incorrect or incomplete information, especially in highly specialized or novel cases.
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<!---### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.--->
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## Getting Started with the Model
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Use the code below to get started with the model.
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## Use with Transformers AutoModelForCausalLM
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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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optim="paged_adamw_8bit",<br>
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save_steps=1000,<br>
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logging_steps=30,<br>
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learning_rate=2e-4,<br>
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weight_decay=0.01,<br>
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fp16=True,<br>
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max_grad_norm=1.0,<br>
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warmup_ratio=0.1<br><!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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<!---#### Speeds, Sizes, Times [optional]
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