Instructions to use Ashish9947/Programming_Question_Answer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Ashish9947/Programming_Question_Answer with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_7b") model = PeftModel.from_pretrained(base_model, "Ashish9947/Programming_Question_Answer") - Notebooks
- Google Colab
- Kaggle
Commit History
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Librarian Bot: Add base_model information to model
This pull request aims to enrich the metadata of your model by adding [`openlm-research/open_llama_7b`](https://huggingface.co/openlm-research/open_llama_7b) as a `base_model` field, situated in the `YAML` block of your model's `README.md`. How did we find this information? We extracted this infromation from the `adapter_config.json` file of your model. **Why add this?** Enhancing your model's metadata in this way: - **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub. - **Highlights Impact** - It showcases the contributions and influences different models have within the community. For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer). This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, quer…0d0f64e
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