metadata
language:
- en
license: apache-2.0
tags:
- gpt2
- pytorch
- causal-lm
- text-generation
- alpaca
- instruction-following
datasets:
- tatsu-lab/alpaca
base_model: koganrath/LiteGPT-Base
LiteGPT-Instruct
This is a 124M parameter Language Model (GPT-2 Small architecture) fine-tuned on the Alpaca dataset for instruction following.
It is part of the "Small Language Model (SLM)" project, trained from scratch on educational data (FineWeb-Edu) and then fine-tuned on instructions.
Model Details
- Architecture: GPT-2 Small (12 layers, 12 heads, 768 embedding dim)
- Parameters: ~124 Million
- Context Length: 1024 tokens
- Training:
- Pre-training: 10B tokens from FineWeb-Edu
- Fine-tuning: Stanford Alpaca dataset (Instruction Tuning)
Usage
This model requires a specific prompt format to function correctly.
Prompt Template (Alpaca)
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{your_instruction}
### Response:
Python Example
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model = GPT2LMHeadModel.from_pretrained("koganrath/LiteGPT-Instruct")
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
instruction = "List three primary colors."
prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:
"""
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Limitations
- Size: As a 124M parameter model, its reasoning capabilities are limited compared to larger models (7B+).
- Hallucinations: It may generate incorrect or nonsensical information.
- Bias: It inherits biases present in the FineWeb and Alpaca datasets.
Authors
Trained by koganrath as part of the LiteGPT Project.