Triangle104/Athena-1-0.5B-Q4_K_S-GGUF

This model was converted to GGUF format from Spestly/Athena-1-0.5B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Athena-1 0.5B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-0.5B-Instruct. Designed for ultra-lightweight applications, Athena-1 0.5B balances compactness with robust performance, making it suitable for tasks with limited computational resources.

    Key Features






    


    ⚡ Ultra-Lightweight and Efficient

Compact Size: With just 500 million parameters, Athena-1 0.5B is ideal for edge devices and low-resource environments. Instruction Following: Fine-tuned for reliable adherence to user instructions. Coding and Mathematics: Capable of handling basic coding and mathematical tasks.

    📖 Contextual Understanding

Context Length: Supports up to 16,384 tokens, enabling processing of moderately sized conversations or documents. Token Generation: Can generate up to 4K tokens of coherent output.

    🌍 Multilingual Support

Supports 20+ languages, including: English, Chinese, French, Spanish, German, Italian, Russian Japanese, Korean, Vietnamese, Thai, and more.

    📊 Structured Data & Outputs

Structured Data Interpretation: Handles formats like tables and JSON effectively. Structured Output Generation: Produces well-formatted outputs for data-specific tasks.

    Model Details

Base Model: Qwen/Qwen2.5-0.5B-Instruct Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. Parameters: 500M total. Layers: (Adjust if different from the base model) Attention Heads: (Adjust if different from the base model) Context Length: Up to 16,384 tokens.

    Applications

Athena-1 0.5B is optimized for:

Conversational AI: Power lightweight and responsive chatbots. Code Assistance: Basic code generation, debugging, and explanations. Mathematical Assistance: Solves fundamental math problems. Document Processing: Summarizes and analyzes smaller documents effectively. Multilingual Tasks: Supports global use cases with a compact model. Structured Data: Reads and generates structured formats like JSON and tables.

    Quickstart

Here’s how you can use Athena-1 0.5B for quick text generation:

Use a pipeline as a high-level helper

from transformers import pipeline

messages = [ {"role": "user", "content": "What can you do?"}, ] pipe = pipeline("text-generation", model="Spestly/Athena-1-0.5B") # Update model name print(pipe(messages))

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-0.5B") # Update model name model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-0.5B") # Update model name

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Athena-1-0.5B-Q4_K_S-GGUF --hf-file athena-1-0.5b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Athena-1-0.5B-Q4_K_S-GGUF --hf-file athena-1-0.5b-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Athena-1-0.5B-Q4_K_S-GGUF --hf-file athena-1-0.5b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Athena-1-0.5B-Q4_K_S-GGUF --hf-file athena-1-0.5b-q4_k_s.gguf -c 2048