Instructions to use ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Nemo-Instruct-2407") model = PeftModel.from_pretrained(base_model, "ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1") - Notebooks
- Google Colab
- Kaggle
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Download README.md from ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
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https://huggingface.co/ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1/resolve/main/README.md
- Command line
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hf download hf://ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1/README.md
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curl -L -o README.md https://huggingface.co/ToastyPigeon/SpringDragon-NeMo-Instruct-QLoRA-ep1/resolve/main/README.md
1.29 kB
metadata
base_model: mistralai/Mistral-Nemo-Instruct-2407
library_name: peft
license: apache-2.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: SpringDragon-NeMo-Instruct-QLoRA-ep1
results: []
SpringDragon-NeMo-Instruct-QLoRA-ep1
This model is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407 on the SpringDragon dataset.
Completion format. User instructions are given with >.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 111
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 1
Framework versions
- PEFT 0.11.1
- Transformers 4.44.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1