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))
