Instructions to use Drew-ch/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Drew-ch/results with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("taide/Llama3-TAIDE-LX-8B-Chat-Alpha1") model = PeftModel.from_pretrained(base_model, "Drew-ch/results") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Drew-ch/results: direct link, hf CLI and curl.
- Browser
- Download file 1.14 kB
-
https://huggingface.co/Drew-ch/results/resolve/main/README.md
- Command line
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hf download hf://Drew-ch/results/README.md
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curl -L -o README.md https://huggingface.co/Drew-ch/results/resolve/main/README.md
1.14 kB
metadata
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: taide/Llama3-TAIDE-LX-8B-Chat-Alpha1
model-index:
- name: results
results: []
results
This model is a fine-tuned version of taide/Llama3-TAIDE-LX-8B-Chat-Alpha1 on an unknown dataset.
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 100
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1