Modelpublic

aayanmishra-ml/Athena-3-14B

sourceHugging Facemitupdated 1y agoView on Hugging Face

3likes38downloads

Model Card

<div align="center"> <span style="font-family: default; font-size: 1.5em;">Athena-3</span> <div> 🚀 Faster, Sharper, Smarter than Athena 1 and Athena 2🌟 </div> </div> <br> <div align="center" style="line-height: 1;"> <a href="https://github.com/Aayan-Mishra/Maverick-Search" style="margin: 2px;"> <img alt="Github Page" src="https://img.shields.io/badge/Toolkit-000000?style=for-the-badge&logo=github&logoColor=000&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://aayanmishra.com/blog/athena-3" target="_blank" style="margin: 2px;"> <img alt="Blogpost" src="https://img.shields.io/badge/Blogpost-%23000000.svg?style=for-the-badge&logo=notion&logoColor=white" style="display: inline-block; vertical-align: middle;"/> </a> <a href="https://huggingface.co/Spestly/Athena-3-14B" style="margin: 2px;"> <img alt="HF Page" src="https://img.shields.io/badge/Athena-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor" style="display: inline-block; vertical-align: middle;"/> </a> </div>

Athena-3

Athena generated this model card!

Athena-3-14B is a 14.0-billion-parameter causal language model fine-tuned from Qwen2.5-14B-Instruct. This model is designed to provide highly fluent, contextually aware, and logically sound outputs across a broad range of NLP and reasoning tasks. It balances instruction-following with generative flexibility.

Model Details

—Model Developer: Aayan Mishra
—Model Type: Causal Language Model
—Architecture: Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, Attention QKV bias, and tied word embeddings
—Parameters: 14.0 billion total (12.84 billion non-embedding)
—Layers: 40
—Attention Heads: 40 for query and 4 for key-value (Grouped Query Attention)
—Vocabulary Size: Approximately 151,646 tokens
—Context Length: Supports up to 131,072 tokens
—Languages Supported: Over 29 languages, including strong performance in English, Chinese, and multilingual instruction tasks
—License: MIT

Training Details

Athena-3-14B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process spanned approximately 90 minutes over 60 epochs, utilizing a curated instruction-tuned dataset. It is tailored for generalist NLP performance with a focus on reasoning, alignment, and fluency.

Intended Use

Athena-3-14B is ideal for a wide variety of tasks, including:

—Instruction Following: Handling complex prompts with step-by-step logical output
—Writing Assistance: Generating essays, emails, and coherent narratives
—NLP Tasks: Summarization, question answering, translation, and text classification
—STEM Support: Reasoning through academic and technical content

While Athena-3-14B is a versatile model, it is not intended for safety-critical applications or the handling of private, sensitive information.

How to Use

To utilize Athena-3-14B, ensure that you have the latest version of the transformers library installed:

bash

pip install transformers

Here's an example of how to load the Athena-3-14B model and generate a response:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Spestly/Athena-3-14B"
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Explain the concept of entropy in thermodynamics."
messages = [
    {"role": "system", "content": "You are Maverick, an AI assistant designed to be helpful."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    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]
print(response)

Maverick Search usage 🔍

To use this model with Maverick Search, please refer to this repository

Limitations

Users should be aware of the following limitations:

—Biases: Athena-3-14B may reflect biases from its pretraining and fine-tuning data. Outputs should be reviewed for fairness and accuracy.
—Knowledge Cutoff: The model's knowledge is current as of August 2024.
—Multilingual Performance: Performance varies by language, with strongest capabilities in English and aligned datasets.

Acknowledgements

Athena-3-14B builds upon the Qwen2.5-14B foundation. Special thanks to the open-source ecosystem and Unsloth for enabling efficient fine-tuning workflows.

License

Athena-3-14B is released under the MIT License, permitting broad use and distribution with proper attribution.

Contact

—Email: maverick@aayanmishra.com