---
language: en
tags:
- arxiv
- research-papers
- text-generation
license: apache-2.0
---

# KnullAI v2 - Fine-tuned on ArXiver Dataset

This model is a fine-tuned version of KnullAI v2, specifically trained on the ArXiver dataset containing research paper information.

## Training Data
The model was fine-tuned on the neuralwork/arxiver dataset, which contains:
- Paper titles
- Abstracts
- Authors
- Publication dates
- Links

## Model Details
- Base model: Rawkney/knullAi_v2
- Training type: Causal language modeling
- Hardware: T4 GPU
- Mixed precision: FP16

## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("YOUR_REPO_ID")
tokenizer = AutoTokenizer.from_pretrained("YOUR_REPO_ID")

# Example usage
title = "Your paper title"
input_text = f"Title: {title}\nAbstract:"
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(
    inputs["input_ids"],
    max_length=256,
    temperature=0.7,
    top_p=0.9,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Training Parameters
- Learning rate: 1e-5
- Epochs: 1
- Batch size: 1
- Gradient accumulation steps: 16
- Mixed precision training (fp16)
- Max sequence length: 512