Instructions to use Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-large20 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-large20 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B-bnb-4bit") model = PeftModel.from_pretrained(base_model, "Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-large20") - Transformers
How to use Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-large20 with Transformers:
# 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/Openi-llama3.1-8B-WeightedLoss-large20")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-large20", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Model Card for Model ID
Basically used to summarize text from the Open-i dataset
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/Openi-llama3.1-8B-WeightedLoss-large20
Base model
meta-llama/Llama-3.1-8B Quantized
unsloth/Meta-Llama-3.1-8B-bnb-4bit