Instructions to use buming/peft-starcoder-lora-a100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use buming/peft-starcoder-lora-a100 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b") model = PeftModel.from_pretrained(base_model, "buming/peft-starcoder-lora-a100") - Notebooks
- Google Colab
- Kaggle
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Download README.md from buming/peft-starcoder-lora-a100: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
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https://huggingface.co/buming/peft-starcoder-lora-a100/resolve/main/README.md
- Command line
-
hf download hf://buming/peft-starcoder-lora-a100/README.md
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curl -L -o README.md https://huggingface.co/buming/peft-starcoder-lora-a100/resolve/main/README.md
1.3 kB
metadata
library_name: peft
license: bigcode-openrail-m
base_model: bigcode/starcoderbase-1b
tags:
- generated_from_trainer
model-index:
- name: peft-starcoder-lora-a100
results: []
peft-starcoder-lora-a100
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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 40
Training results
Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.0.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3