language, license, tags, datasets, base_model

language	license	tags	datasets	base_model
en	apache-2.0	gpt2 pytorch causal-lm text-generation alpaca instruction-following	tatsu-lab/alpaca	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.