PEFT
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Librarian Bot: Add base_model information to model

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This pull request aims to enrich the metadata of your model by adding [`meta-llama/Llama-2-70b-hf`](https://huggingface.co/meta-llama/Llama-2-70b-hf) 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, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien).

If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!

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  1. README.md +5 -4
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  ---
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- library_name: peft
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- license: cc-by-4.0
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- datasets:
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- - iamshnoo/alpaca-cleaned-chinese
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  language:
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  - zh
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  - en
 
 
 
 
 
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  ---
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  This represents the PEFT weights only. The base model is LLaMA 2. Instruction finetuning was done using 4 bit QLoRA on a single A100 GPU with the PEFT config as given below. The dataset used for this instruction finetuning process is a translated version of the cleaned alpaca dataset (translated using NLLB-1.3B).
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  ---
 
 
 
 
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  language:
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  - zh
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  - en
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+ license: cc-by-4.0
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+ library_name: peft
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+ datasets:
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+ - iamshnoo/alpaca-cleaned-chinese
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+ base_model: meta-llama/Llama-2-70b-hf
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  ---
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  This represents the PEFT weights only. The base model is LLaMA 2. Instruction finetuning was done using 4 bit QLoRA on a single A100 GPU with the PEFT config as given below. The dataset used for this instruction finetuning process is a translated version of the cleaned alpaca dataset (translated using NLLB-1.3B).
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