Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 61
Question Answering
Transformers
Safetensors
PEFT
llama-3
4bit
unsloth
lora
medical
bf16
a100
bitsandbytes
fine-tuned
healthcare
instruction-tuned
chat
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SujitShelar/llama3-medchat-8b-lora", device_map="auto")Quick Links
Model Card for Model ID
A 4-bit quantized, LoRA-adapted version of Meta's LLaMA 3 8B model, fine-tuned on the medalpaca/medical_meadow_medical_flashcards dataset for medical question-answering tasks. This model is optimized for efficient inference and training on hardware like NVIDIA A100 GPUs using BF16 precision.
Model Details
Base model: unsloth/llama-3-8b-bnb-4bit
Fine-tuned by: Sujit Shelar
Model type: Auto-regressive transformer (decoder-only)
Quantization: 4-bit NF4 via bitsandbytes
PEFT: LoRA (r=4, alpha=8, dropout=0.01)
Language: English
License: LLaMA 3 Community License
Model Description
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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Model Sources [optional]
- Repository: SujitShelar/llama3-medchat-8b-lora
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Uses
Direct Use
This model is intended for generating concise, accurate answers to medical questions, making it suitable for applications like:
Medical education tools
Clinical decision support systems
Healthcare chatbots
Medical flashcard applications
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Out-of-Scope Use
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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
Training Data
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Training Procedure
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Training Hyperparameters
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Evaluation
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Factors
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Results
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Summary
Model Examination [optional]
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="SujitShelar/llama3-medchat-8b-lora")