Instructions to use aayanmishra-ml/Athena-1-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aayanmishra-ml/Athena-1-7B with Transformers:

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

pipe = pipeline("text-generation", model="aayanmishra-ml/Athena-1-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aayanmishra-ml/Athena-1-7B")
model = AutoModelForCausalLM.from_pretrained("aayanmishra-ml/Athena-1-7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aayanmishra-ml/Athena-1-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aayanmishra-ml/Athena-1-7B"
# 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-1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aayanmishra-ml/Athena-1-7B

SGLang

How to use aayanmishra-ml/Athena-1-7B 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-1-7B" \
    --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-1-7B",
		"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-1-7B" \
        --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-1-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use aayanmishra-ml/Athena-1-7B with Docker Model Runner:
```
docker model run hf.co/aayanmishra-ml/Athena-1-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Athena-1: Lightweight and Powerful Instruction-Following Model

Athena-1 is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-7B-Instruct. Designed to balance efficiency and performance, Athena 7B provides powerful text-generation capabilities, making it suitable for a variety of real-world applications, including conversational AI, content creation, and structured data processing.

Key Features

🚀 Enhanced Performance

Instruction Following: Fine-tuned for excellent adherence to user prompts and instructions.
Coding and Mathematics: Proficient in solving coding problems and mathematical reasoning.
Lightweight: At 7.62 billion parameters, Athena-1-7B offers powerful performance while maintaining efficiency.

📖 Long-Context Understanding

Context Length: Supports up to 128K tokens, ensuring accurate handling of large documents or conversations.
Token Generation: Can generate up to 8K tokens of output.

🌍 Multilingual Support

Supports 29+ languages, including:
- English, Chinese, French, Spanish, Portuguese, German, Italian, Russian
- Japanese, Korean, Vietnamese, Thai, Arabic, and more.

📊 Structured Data & Outputs

Structured Data Interpretation: Understands and processes structured formats like tables and JSON.
Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats.

Model Details

Base Model: Qwen/Qwen2.5-7B-Instruct
Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
Parameters: 7.62B total (6.53B non-embedding).
Layers: 28
Attention Heads: 28 for Q, 4 for KV.
Context Length: Up to 131,072 tokens.

Applications

Athena-1 is designed for a broad range of use cases:

Conversational AI: Create natural, human-like chatbot experiences.
Code Generation: Generate, debug, or explain code snippets.
Mathematical Problem Solving: Assist with complex calculations and reasoning.
Document Processing: Summarize or analyze large documents.
Multilingual Applications: Support for diverse languages for translation and global use cases.
Structured Data: Process and generate structured data, including tables and JSON.

Quickstart

Here’s how you can use Athena 7B for quick text generation:

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

messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="Spestly/Athena-1-7B")
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-7B")
model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-7B")