N3N_gemma-2-9b-it_20241029_1532

Model Overview

• Base Model: unsloth/gemma-2-9b-it
• License: gemma
• Parameters: 10.2B
• Language: English
• Training Framework: Unsloth + Huggingface TRL

Achievement: #1 Ranking for 9B and 12B LLMs (November 8, 2024)

Introduction

N3N_gemma-2-9b-it_20241029_1532 is a 10.2B parameter open-source model built upon Gemma2-9B-Instruct through additional training. What sets this model apart is its fine-tuning process using a high-quality dataset derived from 1.6 million arXiv papers.

Key Features

• High-quality Dataset: The model has been fine-tuned using a comprehensive dataset compiled from 1.6 million arXiv papers, ensuring robust performance across various real-world applications.
• Superior Reasoning: The model demonstrates exceptional performance in mathematical reasoning and complex problem-solving tasks, outperforming comparable models in these areas.

This model represents our commitment to advancing language model capabilities through meticulous dataset preparation and continuous model enhancement.

Quickstart

Here is a code snippet with apply_chat_template, showing how to load the tokenizer and model and generate content. This method simplifies structuring conversation prompts by adding generation-specific prompts automatically.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained(
    "nhyha/N3N_gemma-2-9b-it_20241029_1532",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("nhyha/N3N_gemma-2-9b-it_20241029_1532")

# `apply_chat_template` formats conversation messages for better model input structure
prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]

# Automatically adds the necessary generation prompt to the message
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(device)

generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

Training Details

Hyperparameters

{
    "seed": 3407,
    "warmup_steps": 50,
    "total_train_batch_size": 512,
    "total_eval_batch_size": 64,
    "learning_rate": 5e-05,
    "optimizer": "adamw_8bit",
    "lr_scheduler_type": "cosine",
    "num_epochs": 3,
    "r": 32,
    "lora_alpha": 32,
    "rs_lora": True,
    "weight_decay": 0.01
}

Open LLM Leaderboard Evaluation Results

Business & Collaboration

Contact

Are you looking for customized LLMs tailored to your business needs? Jikji Labs offers advanced infrastructure including H100*8 GPU clusters for optimal model training and deployment. Our expertise spans:

• Large-scale data processing
• High-performance GPU computing
• Custom model development and training

We welcome collaborations and are always eager to hear your feedback or discuss potential partnerships. Visit our website to learn how our infrastructure and expertise can drive your AI initiatives forward.

Collaborations

We are actively seeking support and investment to further our development of robust language models, with a focus on building high-quality and specialized datasets to cater to a wide range of applications. Our expertise in dataset generation enables us to create models that are precise and adaptable to specific business requirements. If you are excited by the opportunity to collaborate and navigate future challenges with us, please visit our website for more information.

Acknowledgement

Special thanks to google for providing the base model to the Open-Source community.