Instructions to use Namanbharti/finetune_starcoder2_with_GO_Data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Namanbharti/finetune_starcoder2_with_GO_Data with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoder2-3b") model = PeftModel.from_pretrained(base_model, "Namanbharti/finetune_starcoder2_with_GO_Data") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Namanbharti/finetune_starcoder2_with_GO_Data: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
-
https://huggingface.co/Namanbharti/finetune_starcoder2_with_GO_Data/resolve/main/README.md
- Command line
-
hf download hf://Namanbharti/finetune_starcoder2_with_GO_Data/README.md
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curl -L -o README.md https://huggingface.co/Namanbharti/finetune_starcoder2_with_GO_Data/resolve/main/README.md
1.29 kB
metadata
license: bigcode-openrail-m
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: bigcode/starcoder2-3b
model-index:
- name: finetune_starcoder2_with_GO_Data
results: []
finetune_starcoder2_with_GO_Data
This model is a fine-tuned version of bigcode/starcoder2-3b 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 16
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.8.2
- Transformers 4.40.0.dev0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.2