Model Card abhilashkrish/nursing-pharmacology:

  • base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
  • library_name: peft
  • license: mit
  • datasets:
    • timzhou99/nursing-pharmacology
  • language:
    • en
  • description:
    • Healthcare Inference Engine

Model Summary

abhilashkrish/nursing-pharmacology is a domain-specific large language model fine-tuned to provide responses for healthcare training and related applications. Designed to assist healthcare professionals, this model excels at generating precise explanations and adhering to controlled-substance protocols. With over 41 million parameters, it leverages state-of-the-art fine-tuning techniques to optimize for domain specificity and accuracy.

Model Details

Model Description

  • Developed by: Abhilash Krishnan
  • Model type: Fine-tuned version of meta-llama-3.1-8b-bnb-4bit
  • Language(s): English
  • License: MIT
  • Finetuned from model: unsloth/meta-llama-3.1-8b-bnb-4bit

Uses

Direct Use

This model can be directly used for:

  • Assisting healthcare professionals with training tasks.
  • Providing detailed explanations for controlled-substance protocols.
  • Serving as an inference engine in EdTech platforms for medical training.

Downstream Use

With further fine-tuning, this model can be adapted for:

  • Broader healthcare applications like patient assistance tools.
  • Domain-specific professional training across industries.

Out-of-Scope Use

  • General-purpose conversation or text generation beyond the healthcare domain.
  • Use cases requiring multilingual or culturally specific nuances outside of English.

Bias, Risks, and Limitations

Bias

  • While the model is fine-tuned on healthcare-specific data, it may not account for regional differences in medical practices or laws.

Risks

  • Incorrect or outdated training data may lead to inaccuracies in responses, particularly in critical medical contexts.

Limitations

  • The model is trained exclusively on English-language datasets and is not designed for multilingual support or non-healthcare applications.

How to Get Started with the Model

Here’s a simple example to load and use the model:

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("abhilashkrish/nursing-pharmacology")
model = AutoModelForCausalLM.from_pretrained("abhilashkrish/nursing-pharmacology")

# Generate a response
input_text = "What are the protocols for handling controlled substances?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=150)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Model Training Details

  • training_details:
    • training_data:
      • dataset: timzhou99/nursing-pharmacology
      • description:
        • The dataset includes text focused on nursing pharmacology and healthcare-specific training materials.
    • training_procedure:
      • preprocessing:
        • Tokenized using Hugging Face's AutoTokenizer with healthcare domain-specific vocabulary.
      • training_regime:
        • Mixed precision with bfloat16 to optimize GPU memory usage and accelerate fine-tuning.
      • hyperparameters:
        • learning_rate: 4e-5
        • batch_size: 8
        • gradient_accumulation_steps: 4
        • epochs: 5
    • compute_infrastructure:
      • hardware: NVIDIA A40 GPU (44 GB VRAM)
      • software:
        • PEFT_library: v0.13.2
        • PyTorch: 2.5.1+cu12
        • CUDA: 12.4
  • evaluation:
    • testing_data:
      • dataset: timzhou99/nursing-pharmacology
      • description:
        • The model was evaluated on a subset of the timzhou99/nursing-pharmacology dataset using specific healthcare-related tasks.
    • metrics:
      • perplexity:
        • Evaluated as a measure of model fluency.
      • domain_specific_accuracy:
        • Assessed based on its ability to generate accurate healthcare protocols.
  • model_card_contact:
  • framework_versions:
    • PEFT: 0.13.2
    • Transformers: 4.46.3

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Dataset used to train abhilashkrish/nursing-pharmacology