Instructions to use iamshnoo/alpaca-2-13b-persian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use iamshnoo/alpaca-2-13b-persian with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-13b-hf") model = PeftModel.from_pretrained(base_model, "iamshnoo/alpaca-2-13b-persian") - Notebooks
- Google Colab
- Kaggle
Commit ·
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Parent(s): ea9105e
Librarian Bot: Add base_model information to model (#1)
Browse files- Librarian Bot: Add base_model information to model (fa6ab54b4ed541d67a679c8e4afecff1c6039ae9)
Co-authored-by: Librarian Bot (Bot) <librarian-bot@users.noreply.huggingface.co>
README.md
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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-persian
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language:
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- fa
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- en
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metrics:
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- accuracy
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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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language:
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- fa
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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-persian
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metrics:
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- accuracy
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base_model: meta-llama/Llama-2-13b-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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