Instructions to use hash-map/got_QA_fine_tuned_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use hash-map/got_QA_fine_tuned_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "hash-map/got_QA_fine_tuned_model") - Transformers
How to use hash-map/got_QA_fine_tuned_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="hash-map/got_QA_fine_tuned_model")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("hash-map/got_QA_fine_tuned_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7f46abc341fc302bc0b0c0825e13060943e2b7e450f76e92b44c479a1a6c54f2
- Size of remote file:
- 117 MB
- SHA256:
- 84045962ad0ef5721b12b953257338c654bdaa5955fd40ca5695d1222bd40757
路
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