Triangle104/Athena-1-3B-Q5_K_M-GGUF

This model was converted to GGUF format from Spestly/Athena-1-3B 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 3B is a fine-tuned, instruction-following large language model derived from Qwen/Qwen2.5-3B-Instruct. It is designed to provide efficient, high-quality text generation while maintaining a compact size. Athena 3B is optimized for lightweight applications, conversational AI, and structured data tasks, making it ideal for real-world use cases where performance and resource efficiency are critical.

Key Features

⚡ Lightweight and Efficient

Compact Size: At just 3.09 billion parameters, Athena-1 3B offers excellent performance with reduced computational requirements. Instruction Following: Fine-tuned for precise and reliable adherence to user prompts. Coding and Mathematics: Proficient in solving coding challenges and handling mathematical tasks.

📖 Long-Context Understanding

Context Length: Supports up to 32,768 tokens, enabling the processing of moderately lengthy 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: Processes structured formats like tables and JSON. Structured Output Generation: Generates well-formatted outputs, including JSON and other structured formats.

Details

Base Model: Qwen/Qwen2.5-3B-Instruct Architecture: Transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias, and tied word embeddings. Parameters: 3.09B total (2.77B non-embedding). Layers: 36 Attention Heads: 16 for Q, 2 for KV. Context Length: Up to 32,768 tokens.

Applications

Athena 3B is designed for a variety of real-world applications:

Conversational AI: Build fast, responsive, and lightweight chatbots. Code Generation: Generate, debug, or explain code snippets. Mathematical Problem Solving: Assist with calculations and reasoning. Document Processing: Summarize and analyze moderately large documents. Multilingual Applications: Support for global use cases with diverse language requirements. Structured Data: Process and generate structured data, such as tables and JSON.

Quickstart

Here’s how you can use Athena 3B for quick text generation:

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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-1-3B") pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-3B") model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-3B")

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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-1-3B-Q5_K_M-GGUF --hf-file athena-1-3b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Athena-1-3B-Q5_K_M-GGUF --hf-file athena-1-3b-q5_k_m.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-1-3B-Q5_K_M-GGUF --hf-file athena-1-3b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Athena-1-3B-Q5_K_M-GGUF --hf-file athena-1-3b-q5_k_m.gguf -c 2048