mkwukong/functiongemma-270m
The mkwukong/functiongemma-270m model is a 0.3 billion parameter language model, fine-tuned from Google's functiongemma-270m-it. It was trained using the TRL framework and features a context length of 32768 tokens. This model is designed for text generation tasks, leveraging its instruction-tuned base for various conversational applications.
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Model Overview
mkwukong/functiongemma-270m is a compact 0.3 billion parameter language model, fine-tuned from the google/functiongemma-270m-it base model. This fine-tuning process was conducted using the TRL library, a framework for Transformer Reinforcement Learning.
Key Capabilities
- Text Generation: The model is capable of generating human-like text based on given prompts, as demonstrated by its quick-start example for answering questions.
- Instruction Following: Inheriting from its instruction-tuned base, it is designed to follow instructions effectively for various tasks.
- Efficient Deployment: With only 270 million parameters, it offers a lightweight solution for applications requiring efficient inference.
Training Details
The model underwent a Supervised Fine-Tuning (SFT) process. The training utilized specific versions of popular machine learning frameworks:
- TRL: 1.14.0
- Transformers: 5.16.1
- Pytorch: 2.11.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.23.1
Good For
This model is suitable for developers looking for a small, efficient language model for text generation and instruction-following tasks, particularly where computational resources are a consideration. Its fine-tuned nature makes it a good candidate for conversational AI and question-answering systems.