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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)