Rawkney/knullAi_v2

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

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