Triangle104/Athena-1-7B-Q4_K_M-GGUF

This model was converted to GGUF format from Spestly/Athena-1-7B 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 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")

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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-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Athena-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_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-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Athena-1-7B-Q4_K_M-GGUF --hf-file athena-1-7b-q4_k_m.gguf -c 2048