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@@ -4,7 +4,7 @@ tags:
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  - llm
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  - llama3
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  - rare disease
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- license: openrail
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  language:
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  - en
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  pipeline_tag: text-generation
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  ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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-
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-
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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
@@ -50,17 +46,19 @@ ReidLM, like all large language models, has inherent biases and limitations that
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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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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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- [More Information Needed]
 
 
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  ## Training Details
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@@ -68,8 +66,6 @@ Use the code below to get started with the model.
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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-
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  ### Training Procedure
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
@@ -80,17 +76,16 @@ Use the code below to get started with the model.
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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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  <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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  <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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  #### Testing Data
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  [More Information Needed]
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- #### Factors
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  <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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  [More Information Needed]
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- #### Metrics
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  <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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  [More Information Needed]
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- ### Results
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  [More Information Needed]
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  [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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@@ -158,7 +153,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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  <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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  [More Information Needed]
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@@ -170,7 +165,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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  <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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  ## More Information [optional]
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  ## Model Card Contact
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- [More Information Needed]
 
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  - llm
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  - llama3
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  - rare disease
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+ license: llama3
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  language:
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  - en
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  pipeline_tag: text-generation
 
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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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  -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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+ This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.
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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
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+
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+
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  ## Training Details
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  <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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  ### Training Procedure
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  <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
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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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  <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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+ <!---## Evaluation
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  <!-- This section describes the evaluation protocols and provides the results. -->
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+ <!---### Testing Data, Factors & Metrics
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  #### Testing Data
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  [More Information Needed]
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+ <!---#### Factors
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  <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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  [More Information Needed]
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+ <!---#### Metrics
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  <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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  [More Information Needed]
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+ <!---### Results
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  [More Information Needed]
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  [More Information Needed]
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+ <!---## Environmental Impact
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+ Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly
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  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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  <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+ <!---**BibTeX:**
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  [More Information Needed]
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  <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+ <!---[More Information Needed]
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  ## More Information [optional]
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  ## Model Card Contact
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+ [More Information Needed]--->