Instructions to use burtenshaw/code-smol2-text-to-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use burtenshaw/code-smol2-text-to-sql with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-1.7B-Instruct") model = PeftModel.from_pretrained(base_model, "burtenshaw/code-smol2-text-to-sql") - Notebooks
- Google Colab
- Kaggle
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Download README.md from burtenshaw/code-smol2-text-to-sql: direct link, hf CLI and curl.
- Browser
- Download file 1.29 kB
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https://huggingface.co/burtenshaw/code-smol2-text-to-sql/resolve/main/README.md
- Command line
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hf download hf://burtenshaw/code-smol2-text-to-sql/README.md
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curl -L -o README.md https://huggingface.co/burtenshaw/code-smol2-text-to-sql/resolve/main/README.md
1.29 kB
metadata
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolLM2-1.7B-Instruct
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: code-smol2-text-to-sql
results: []
code-smol2-text-to-sql
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-1.7B-Instruct on the generator 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1