Triangle104
/

Athena-1-14B-Q5_K_M-GGUF

@@ -28,77 +28,30 @@ 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.
 		Model 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).
@@ -106,17 +59,7 @@ 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:
@@ -128,21 +71,10 @@ 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
@@ -160,56 +92,25 @@ 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/},

 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.
 		Model 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).
 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:
 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
 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/},