Triangle104
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Athena-1-14B-Q4_K_S-GGUF

@@ -26,79 +26,29 @@ 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.
-		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).
@@ -106,21 +56,10 @@ 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.
@@ -128,21 +67,10 @@ 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 +88,26 @@ 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/},

 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).
 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.
 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/},