# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Chilliwiddit/AsclepiusLM", dtype="auto")Quick Links
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Basically paired the unsloth/Meta-Llama-3.1-8B-bnb-4bit base model with the fine-tuned Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-small2 adapter.
Training Details
Training Data
I used the Open-i dataset
Training Hyperparameters
Training regime: [More Information Needed]
16 Mixed Precision
LR of 0.0-1
5 Epochs
lambda medical weight of 20 and lambda negation weight of 20
Used 2nd iteration of summary medical concepts file
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Model tree for Chilliwiddit/AsclepiusLM
Base model
meta-llama/Llama-3.1-8B Quantized
unsloth/Meta-Llama-3.1-8B-bnb-4bit
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("summarization", model="Chilliwiddit/AsclepiusLM")