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README.md
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pipeline_tag: text-generation
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# Model Card for
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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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<!-- 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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### 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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# 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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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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