Semestra2.0

Model Card: Semestra 2.0

1. Model Details

• Model Name: Semestra 2.0 (micymike/Semestra2.0)
• Model Type: Causal Language Model (LLM)
• Domain: Education and Adaptive Learning

2. Intended Use

• Primary Use Cases: Designed specifically for agentic workflows within educational applications. The model is capable of autonomously navigating educational tools, breaking down complex topics, and generating instructional content.
• Adaptive Learning: Semestra is highly adaptable to fit individual user needs, making it exceptionally well-suited for students. It can adjust its tone, reading level, and explanation style based on the student's proficiency.
• Target Audience: Ed-tech developers, students, teachers, and educational researchers.

3. Capabilities

• Personalized Tutoring: Can act as an AI tutor that adapts to a student's learning pace.
• Agentic Orchestration: Can be integrated into larger ed-tech systems to manage multi-step workflows (e.g., assessing a student's weak points, gathering resources, and creating a customized study plan).
• Question Answering: Provides clear, accurate, and context-aware answers to academic queries.

4. Limitations and Biases

• Factuality: Like all LLMs, Semestra may occasionally hallucinate or provide factually incorrect information. It should be used as a supplementary educational tool, and students should be encouraged to verify critical facts.
• Safety and Moderation: While tuned for education, developers should implement standard guardrails and safety filters when deploying this model in student-facing applications to prevent the generation of inappropriate content.

5. How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("micymike/Semestra2.0")
model = AutoModelForCausalLM.from_pretrained("micymike/Semestra2.0", device_map="auto")

prompt = "Explain quantum physics to a high school student."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))