Instructions to use aayanmishra-ml/Athena-3-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aayanmishra-ml/Athena-3-14B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="aayanmishra-ml/Athena-3-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aayanmishra-ml/Athena-3-14B")
model = AutoModelForCausalLM.from_pretrained("aayanmishra-ml/Athena-3-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aayanmishra-ml/Athena-3-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aayanmishra-ml/Athena-3-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Athena-3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aayanmishra-ml/Athena-3-14B

SGLang

How to use aayanmishra-ml/Athena-3-14B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "aayanmishra-ml/Athena-3-14B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Athena-3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "aayanmishra-ml/Athena-3-14B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aayanmishra-ml/Athena-3-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use aayanmishra-ml/Athena-3-14B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aayanmishra-ml/Athena-3-14B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for aayanmishra-ml/Athena-3-14B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for aayanmishra-ml/Athena-3-14B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aayanmishra-ml/Athena-3-14B",
    max_seq_length=2048,
)

Docker Model Runner
How to use aayanmishra-ml/Athena-3-14B with Docker Model Runner:
```
docker model run hf.co/aayanmishra-ml/Athena-3-14B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Athena-3

🚀 Faster, Sharper, Smarter than Athena 1 and Athena 2🌟

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:

pip install transformers

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

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.