Triangle104/Athena-1-14B-Q4_K_S-GGUF

This model was converted to GGUF format from Spestly/Athena-1-14B 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 is a state-of-the-art language model fine-tuned from Qwen/Qwen2.5-14B-Instruct. Designed to excel in instruction-following tasks, Athena 1 delivers advanced capabilities in text generation, coding, mathematics, and long-context understanding. It is optimized for a wide variety of use cases, including conversational AI, structured data interpretation, and multilingual applications. It outperforms Ava 1.5 in many aspects making Athena-1 the superior model.

Key Features

🚀 Enhanced Capabilities

Instruction Following: Athena 1 has been fine-tuned for superior adherence to user prompts, making it ideal for chatbots, virtual assistants, and guided workflows. Coding and Mathematics: Specialized fine-tuning enhances coding problem-solving and mathematical reasoning. Long-Context Understanding: Handles input contexts up to 128K tokens and generates up to 8K tokens.

🌐 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, XML, and other structured formats.

Details

Base Model: Qwen/Qwen2.5-14B-Instruct Architecture: Transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. Parameters: 14.7B total (13.1B non-embedding). Layers: 48 Attention Heads: 40 for Q, 8 for KV. Context Length: Up to 131,072 tokens.

Applications

Athena 1 is designed for a wide range of use cases:

Conversational AI and chatbots. Code generation, debugging, and explanation. Mathematical problem-solving. Large-document summarization and analysis. Multilingual text generation and translation. Structured data processing (e.g., tables, JSON).

Quickstart - Below is an example of how to use Athena 1 for text generation:

huggingface-cli login

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-1-14B") pipe(messages)

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Spestly/Athena-1-14B") model = AutoModelForCausalLM.from_pretrained("Spestly/Athena-1-14B")

Performance - Athena 1 has been optimized for efficiency and performance on modern GPUs. For detailed evaluation metrics (e.g., throughput, accuracy, and memory requirements), refer to the Qwen2.5 performance benchmarks.

Requirements - To use Athena 1, ensure the following:

Python >= 3.8 Transformers >= 4.37.0 (to support Qwen models) PyTorch >= 2.0 GPU with BF16 support for optimal performance.

Citation -

If you use Athena 1 in your research or projects, please cite its base model Qwen2.5 as follows:

@misc{qwen2.5, title = {Qwen2.5: A Party of Foundation Models}, url = {https://qwenlm.github.io/blog/qwen2.5/}, author = {Qwen Team}, month = {September}, year = {2024} }

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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-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-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-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Athena-1-14B-Q4_K_S-GGUF --hf-file athena-1-14b-q4_k_s.gguf -c 2048