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
| datasets: | |
| - ewof/code-alpaca-instruct-unfiltered | |
| library_name: peft | |
| tags: | |
| - open-llama | |
| - llama | |
| - code | |
| - instruct | |
| - instruct-code | |
| - code-alpaca | |
| - alpaca-instruct | |
| - alpaca | |
| - llama7b | |
| 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). | |
| This dataset is HuggingFaceH4/CodeAlpaca_20K unfiltered, removing 36 instances of blatant alignment. | |
| The finetuning session got completed in 75 minutes and costed us only `$3` for the entire finetuning run! | |
| Loss metrics: | |
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