Instructions to use aayanmishra-ml/Athena-R3-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aayanmishra-ml/Athena-R3-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="aayanmishra-ml/Athena-R3-1.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aayanmishra-ml/Athena-R3-1.5B")
model = AutoModelForCausalLM.from_pretrained("aayanmishra-ml/Athena-R3-1.5B")
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-R3-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aayanmishra-ml/Athena-R3-1.5B"
# 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-R3-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/aayanmishra-ml/Athena-R3-1.5B

SGLang

How to use aayanmishra-ml/Athena-R3-1.5B 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-R3-1.5B" \
    --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-R3-1.5B",
		"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-R3-1.5B" \
        --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-R3-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use aayanmishra-ml/Athena-R3-1.5B 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-R3-1.5B 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-R3-1.5B 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-R3-1.5B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="aayanmishra-ml/Athena-R3-1.5B",
    max_seq_length=2048,
)

Docker Model Runner
How to use aayanmishra-ml/Athena-R3-1.5B with Docker Model Runner:
```
docker model run hf.co/aayanmishra-ml/Athena-R3-1.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Athena-R3

🚀 Athena-R3: Think Deeper. Solve Smarter. 🤔

Generated by Athena-3!

Model Overview

Athena-R3-1.5B is a 1.5-billion-parameter causal language model fine-tuned from DeepSeek-R1-Distill-Qwen-1.5B. This model is specifically tailored to enhance reasoning capabilities, making it adept at handling complex problem-solving tasks and providing coherent, contextually relevant responses.

Model Details

Model Developer: Aayan Mishra
Model Type: Causal Language Model
Architecture: Transformer with Rotary Position Embeddings (RoPE), SwiGLU activation, RMSNorm, and Attention QKV bias
Parameters: 1.5 billion total
Layers: 24
Attention Heads: 16 for query and 2 for key-value (Grouped Query Attention)
Vocabulary Size: Approximately 151,646 tokens
Context Length: Supports up to 128,000 tokens
Languages Supported: Primarily English, with capabilities in other languages
License: MIT

Training Details

Athena-R3-1.5B was fine-tuned using the Unsloth framework on a single NVIDIA A100 GPU. The fine-tuning process involved 60 epochs over approximately 90 minutes, utilizing a curated dataset focused on reasoning tasks, including mathematical problem-solving and logical inference. This approach aimed to bolster the model's proficiency in complex reasoning and analytical tasks.

Intended Use

Athena-R3-1.5B is designed for a variety of applications, including but not limited to:

Advanced Reasoning: Assisting with complex problem-solving and logical analysis.
Academic Support: Providing explanations and solutions for mathematical and scientific queries.
General NLP Tasks: Engaging in text completion, summarization, and question-answering tasks.
Data Interpretation: Offering insights and explanations for data-centric inquiries.

While Athena-R3-1.5B is a powerful tool for various applications, it is not intended for real-time, safety-critical systems or for processing sensitive personal information.

How to Use

To utilize Athena-R3-1.5B, 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-R3-1.5B model and generate a response:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Spestly/Athena-R3-1.5B"
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 Athena, 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)

Limitations

Users should be aware of the following limitations:

Biases: Athena-R3-1.5B may exhibit biases present in its training data. Users should critically assess outputs, especially in sensitive contexts.
Knowledge Cutoff: The model's knowledge is current up to August 2024. It may not be aware of events or developments occurring after this date.
Language Support: While the model supports multiple languages, performance is strongest in English.

Acknowledgements

Athena-R3-1.5B builds upon the work of the DeepSeek team, particularly the DeepSeek-R1-Distill-Qwen-1.5B model. Gratitude is also extended to the open-source AI community for their contributions to tools and frameworks that facilitated the development of Athena-R3-1.5B.