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  pipeline_tag: text-generation
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  ---
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- # Model Card for Model ID
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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 Details
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  ### Model Description
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  - **Developed by:** MSRIT
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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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  <!-- This should link to a Dataset Card if possible. -->
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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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  <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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  <!---## Environmental Impact
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  pipeline_tag: text-generation
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  ---
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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 Details
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+
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  ### Model Description
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  - **Developed by:** MSRIT
 
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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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+ ```
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+ import transformers
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ # Load the pre-trained model and tokenizer
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+ model_name = "Tanvi03/ReidLM"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name)
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+ model = AutoModelForCausalLM.from_pretrained(model_name)
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+ def generate_text(prompt, max_length=1000):
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ outputs = model.generate(inputs.input_ids, max_length=max_length, num_return_sequences=1)
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+ return tokenizer.decode(outputs[0], skip_special_tokens=True)
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+
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+ prompt = "Explain MEN-1 with respect to how it affects the pituitary gland. What is the other name for this syndrome?"
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+ generated_text = generate_text(prompt)
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+ print(generated_text)
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+ ```
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  <!-- This should link to a Dataset Card if possible. -->
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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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  <!-- Relevant interpretability work for the model goes here -->
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+ <!--[More Information Needed]
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  <!---## Environmental Impact
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