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@@ -12,7 +12,7 @@ pipeline_tag: text-generation
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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 [optional]:** 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.
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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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- ## How to Get 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:** [More Information Needed] <!--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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  # 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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