Instructions to use Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-small1 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-small1 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-small1") - Transformers
How to use Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-small1 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-small1")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Chilliwiddit/Openi-llama3.1-8B-WeightedLoss-small1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a4444221bfab118da663034356c774b6182342cd7bf316cd850796659dd27cfd
- Size of remote file:
- 336 MB
- SHA256:
- 00596ddadbd59cacd126d9689c150c25acb504ff7537d42c137a15d10f281550
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