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

python

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