tinyllama-1.1b

Overview

tinyllama-1.1b is a compact and efficient transformer-based model designed for high-performance language tasks. It is optimized for resource-constrained environments while maintaining robust accuracy across a variety of natural language processing benchmarks.

Features

• Size: 1.1 billion parameters
• Efficiency: Designed for low-latency inference
• Compatibility: Easily extendable with techniques like LoRA for fine-tuning

Usage

To load and use the base model, you can use the following code snippet:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model
base_model_path = "path/to/tinyllama-1.1b"
model = AutoModelForCausalLM.from_pretrained(base_model_path)
tokenizer = AutoTokenizer.from_pretrained(base_model_path)

# Example usage
inputs = tokenizer("Hello, world!", return_tensors="pt")
outputs = model.generate(inputs["input_ids"])
print(tokenizer.decode(outputs[0]))