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README.md

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 This model was converted to GGUF format from [`Spestly/Athena-1-3B`](https://huggingface.co/Spestly/Athena-1-3B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/Spestly/Athena-1-3B) for more details on the model.
 
----
-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:
-
----
-# 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")
-
----
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)