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---
language: en
license: apache-2.0
tags:
- mathematics
- chain-of-thought
- question-answering
---
# KnullAI v2 - Fine-tuned on GAIR/o1-journey
This model is a fine-tuned version of KnullAI v2, specifically trained on mathematical problem-solving using the GAIR/o1-journey dataset.
## Training Data
The model was fine-tuned on the GAIR/o1-journey dataset, which contains:
- Mathematical questions
- Detailed answers
- Step-by-step explanations (Chain of Thought)
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("Rawkney/knullAi_v2")
tokenizer = AutoTokenizer.from_pretrained("Rawkney/knullAi_v2")
# Example usage
question = "What is the area of a triangle with vertices at (0,0), (3,0), and (0,4)?"
input_text = f"Question: {question}\nAnswer:"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(
inputs["input_ids"],
max_length=512,
temperature=0.7,
top_p=0.9
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
## Training Procedure
- Fine-tuned using the Transformers library
- Training parameters:
- Learning rate: 2e-5
- Epochs: 3
- Batch size: 2
- Gradient accumulation steps: 4
- Mixed precision training (fp16)
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