Instructions to use gvij/open-llama-7b-code-alpaca-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gvij/open-llama-7b-code-alpaca-instruct 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, "gvij/open-llama-7b-code-alpaca-instruct") - Notebooks
- Google Colab
- Kaggle
Librarian Bot: Add base_model information to model
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by librarian-bot - opened
README.md
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---
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datasets:
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- ewof/code-alpaca-instruct-unfiltered
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library_name: peft
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tags:
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- open-llama
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- alpaca-instruct
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- alpaca
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- llama7b
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---
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We finetuned Open Llama 7B on Code-Alpaca-Instruct Dataset (ewof/code-alpaca-instruct-unfiltered) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
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---
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library_name: peft
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tags:
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- open-llama
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- alpaca-instruct
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- alpaca
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- llama7b
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datasets:
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- ewof/code-alpaca-instruct-unfiltered
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base_model: openlm-research/open_llama_7b
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---
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We finetuned Open Llama 7B on Code-Alpaca-Instruct Dataset (ewof/code-alpaca-instruct-unfiltered) for 3 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
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