Instructions to use monsterapi/OpenPlatypus_LLAMA2_7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use monsterapi/OpenPlatypus_LLAMA2_7b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "monsterapi/OpenPlatypus_LLAMA2_7b") - Notebooks
- Google Colab
- Kaggle
File size: 1,316 Bytes
8703458 89c4ef8 bea3897 8703458 89c4ef8 8703458 89c4ef8 8703458 89c4ef8 11bec35 89c4ef8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ---
library_name: peft
tags:
- meta-llama/Llama-2-7b-hf
- code
- instruct
- instruct-code
- logical-reasoning
- Platypus2
datasets:
- garage-bAInd/Open-Platypus
base_model: meta-llama/Llama-2-7b-hf
---
We finetuned Meta-Llama/Llama-2-7b-hf on the Open-Platypus dataset (garage-bAInd/Open-Platypus) for 5 epochs using [MonsterAPI](https://monsterapi.ai) no-code [LLM finetuner](https://docs.monsterapi.ai/fine-tune-a-large-language-model-llm).
#### About OpenPlatypus Dataset
OpenPlatypus is focused on improving LLM logical reasoning skills and was used to train the Platypus2 models. The dataset is comprised of various sub-datasets, including PRM800K, ScienceQA, SciBench, ReClor, TheoremQA, among others. These were filtered using keyword search and Sentence Transformers to remove questions with a similarity above 80%. The dataset includes contributions under various licenses like MIT, Creative Commons, and Apache 2.0.
The finetuning session got completed in 1 hour and 30 minutes and costed us only `$15` for the entire finetuning run!
#### Hyperparameters & Run details:
- Model Path: meta-llama/Llama-2-7b-hf
- Dataset: garage-bAInd/Open-Platypus
- Learning rate: 0.0002
- Number of epochs: 5
- Data split: Training: 90% / Validation: 10%
- Gradient accumulation steps: 1
---
license: apache-2.0
---
|