Instructions to use Naveengo/gpt2-medium-on-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Naveengo/gpt2-medium-on-sql-create-context with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("gpt2-medium") model = PeftModel.from_pretrained(base_model, "Naveengo/gpt2-medium-on-sql-create-context") - Notebooks
- Google Colab
- Kaggle
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**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
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## Training Data
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the model is trained on 'b-mc2/sql-create-context' dataset upto 5000rows
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**Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective.
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## Parameter-Efficient Fine-tuning (PEFT)
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Parameter-Efficient Fine-tuning (PEFT) is a technique used to improve the performance of pre-trained language models (LLMs) on specific downstream tasks without fine-tuning all the model's parameters. This is done by freezing most of the model's parameters and only fine-tuning a small number of parameters that are specific to the downstream task.
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## Training Data
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the model is trained on 'b-mc2/sql-create-context' dataset upto 5000rows
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