Instructions to use TheophileCA/OPL_Trained_Starcoder_1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use TheophileCA/OPL_Trained_Starcoder_1B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "TheophileCA/OPL_Trained_Starcoder_1B") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TheophileCA/OPL_Trained_Starcoder_1B: direct link, hf CLI and curl.
- Browser
- Download file 1.16 kB
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https://huggingface.co/TheophileCA/OPL_Trained_Starcoder_1B/resolve/main/README.md
- Command line
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hf download hf://TheophileCA/OPL_Trained_Starcoder_1B/README.md
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curl -L -o README.md https://huggingface.co/TheophileCA/OPL_Trained_Starcoder_1B/resolve/main/README.md
1.16 kB
metadata
base_model: bigcode/starcoderbase-1b
library_name: peft
license: bigcode-openrail-m
tags:
- generated_from_trainer
model-index:
- name: OPL_Trained_Starcoder_1B
results: []
OPL_Trained_Starcoder_1B
This model is a fine-tuned version of bigcode/starcoderbase-1b 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: 0.0005
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 1
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1