Instructions to use stojchet/kto-python-6k-bad-ds with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use stojchet/kto-python-6k-bad-ds with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-1.3b-base") model = PeftModel.from_pretrained(base_model, "stojchet/kto-python-6k-bad-ds") - Notebooks
- Google Colab
- Kaggle
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Download README.md from stojchet/kto-python-6k-bad-ds: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
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https://huggingface.co/stojchet/kto-python-6k-bad-ds/resolve/main/README.md
- Command line
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hf download hf://stojchet/kto-python-6k-bad-ds/README.md
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curl -L -o README.md https://huggingface.co/stojchet/kto-python-6k-bad-ds/resolve/main/README.md
1.26 kB
metadata
base_model: deepseek-ai/deepseek-coder-1.3b-base
library_name: peft
license: other
tags:
- trl
- kto
- generated_from_trainer
model-index:
- name: kto-python-6k-bad-ds
results: []
kto-python-6k-bad-ds
This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on the None 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Framework versions
- PEFT 0.10.0
- Transformers 4.42.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1