HamAI Science Model
A lightweight language model designed to answer science questions clearly and accurately in English.
Overview
HamAI Science Model is trained and fine-tuned on science question–answer datasets. It is built to provide straightforward explanations across topics like physics, chemistry, and biology.
Features
- Focused on science Q&A
- Clear and simple English answers
- Lightweight and efficient
- Suitable for educational use
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Haamipromax/HamAI-Science-1b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
input_text = "Explain how quantum entanglement violates classical locality."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Data
The model was trained on a mix of science question–answer datasets, including:
- General science questions
- Educational textbooks
- Scientific materials
Limitations
- May produce incorrect answers outside science topics
- Not suitable for advanced research-level questions
Intended Use
- Students learning science
- Simple question answering systems
- Educational tools and assistants
License
Apache-2.0