README.md

---
base_model:
- Qwen/Qwen2.5-14B-Instruct
license: mit
language:
- en
- zh
- fr
- es
- pt
- de
- it
- ru
- ja
- ko
- vi
- th
- ar
- fa
- he
- tr
- cs
- pl
- hi
- bn
- ur
- id
- ms
- lo
- my
- ceb
- km
- tl
- nl
tags:
- chemistry
- biology
- code
- text-generation-inference
- STEM
- unsloth
- text-generation-inference
- transformers
- qwen2
- trl
---
<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;">
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  </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](https://github.com/Aayan-Mishra/Maverick-Search)

## **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