DeepSeek Math Tutor
This model is a fine-tuned version of DeepSeek-R1-Distill-Llama-8B optimized for mathematics education. It's designed to provide step-by-step explanations for mathematical problems in a beginner-friendly way.
Model Details
- Base Model: DeepSeek-R1-Distill-Llama-8B
- Training Data: Math reasoning dataset with 7000 examples
- Task: Mathematics education and problem-solving
- Training Method: LoRA fine-tuning
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("analist/deepseek-math-tutor-fine-tuned")
tokenizer = AutoTokenizer.from_pretrained("analist/deepseek-math-tutor-fine-tuned")
# Example prompt format
prompt = '''Below is an instruction that describes a task, paired with an input that provides further context.
Write a response that appropriately completes the request.
### Instruction:
You are a maths expert with advanced knowledge in pedagogy, arithmetics, geometry, analysis, calculus.
Please answer the following questions.
### Question:
{your_math_question}
### Response:
'''
# Generate response
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=1200)
response = tokenizer.decode(outputs[0])
Training Details
- Training framework: Unsloth
- Optimizer: AdamW 8-bit
- Learning rate: 2e-4
- Batch size: 2 (per device)
- Gradient accumulation steps: 4
- Training steps: 60
- Warmup steps: 5
Limitations
This model is specifically tuned for mathematics education and may not perform as well on other tasks. It's designed to provide explanations suitable for beginners learning mathematics.