Instructions to use huiwonLee/sft_v4_reverse_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use huiwonLee/sft_v4_reverse_v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("davidkim205/nox-solar-10.7b-v4") model = PeftModel.from_pretrained(base_model, "huiwonLee/sft_v4_reverse_v2") - Notebooks
- Google Colab
- Kaggle
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Download README.md from huiwonLee/sft_v4_reverse_v2: direct link, hf CLI and curl.
- Browser
- Download file 1.39 kB
-
https://huggingface.co/huiwonLee/sft_v4_reverse_v2/resolve/main/README.md
- Command line
-
hf download hf://huiwonLee/sft_v4_reverse_v2/README.md
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curl -L -o README.md https://huggingface.co/huiwonLee/sft_v4_reverse_v2/resolve/main/README.md
1.39 kB
metadata
license: apache-2.0
base_model: davidkim205/nox-solar-10.7b-v4
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: sft_v4_reverse_v2
results: []
library_name: peft
sft_v4_reverse_v2
This model is a fine-tuned version of davidkim205/nox-solar-10.7b-v4 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.5289
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.6613 | 1.0 | 769 | 1.5289 |
Framework versions
- PEFT 0.5.0
- Transformers 4.39.3
- Pytorch 2.1.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2