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README.yaml

@@ -1,4 +1,3 @@
----
 # Model Card for Legion Coder 8M
 # YAML Front Matter for Hugging Face Hub
 
@@ -7,126 +6,125 @@ library_name: transformers
 license: mit
 pipeline_tag: text-generation
 language:
-  - en
-  - code
+- en
+- code
 tags:
-  - transformers
-  - pytorch
-  - safetensors
-  - text-generation
-  - code-generation
-  - python
-  - javascript
-  - coding
-  - programming
-  - sagemaker
-  - amazon-sagemaker
-  - cpu
-  - compact
-  - efficient
-  - nvdya-kit
-  - death-legion
+- transformers
+- pytorch
+- safetensors
+- text-generation
+- code-generation
+- python
+- javascript
+- coding
+- programming
+- sagemaker
+- amazon-sagemaker
+- cpu
+- compact
+- efficient
+- nvdya-kit
+- death-legion
 
 datasets:
-  - the-stack-v2
+- the-stack-v2
 
 metrics:
-  - perplexity
-  - accuracy
+- perplexity
+- accuracy
 
 model-index:
-  - name: Legion Coder 8M
-    results: []
+- name: Legion Coder 8M
+results: []
 
 inference:
-  parameters:
-    temperature: 0.8
-    top_p: 0.95
-    top_k: 50
-    max_new_tokens: 200
+parameters:
+temperature: 0.8
+top_p: 0.95
+top_k: 50
+max_new_tokens: 200
 
 sagemaker:
-  sdk_version: "2.200.0"
-  instance_type: "ml.m5.large"
-  instance_count: 1
-  container_image: "huggingface-pytorch-inference:2.0.0-transformers4.28.1-cpu-py310-ubuntu20.04-v1.0"
+sdk_version: "2.200.0"
+instance_type: "ml.m5.large"
+instance_count: 1
+container_image: "huggingface-pytorch-inference:2.0.0-transformers4.28.1-cpu-py310-ubuntu20.04-v1.0"
 
 # Model Details
 model_details:
-  name: Legion Coder 8M
-  version: 1.0.0
-  description: A compact yet powerful 44M parameter transformer model optimized for coding tasks
-  developer: DEATH LEGION
-  powered_by: nvdya-kit
-  architecture: GPT-style Transformer
-  parameters: 44,341,632
-  model_size: 170MB
-  hidden_size: 576
-  num_layers: 13
-  num_heads: 16
-  context_length: 1024
-  vocabulary_size: 16000
-  format: Safetensors
-  precision: float32
+name: Legion Coder 8M
+version: 1.0.0
+description: A compact yet powerful 44M parameter transformer model optimized for coding tasks
+developer: DEATH LEGION
+powered_by: nvdya-kit
+architecture: GPT-style Transformer
+parameters: 44,341,632
+model_size: 170MB
+hidden_size: 576
+num_layers: 13
+num_heads: 16
+context_length: 1024
+vocabulary_size: 16000
+format: Safetensors
+precision: float32
 
 # Training Details
 training_details:
-  optimizer: AdamW
-  learning_rate: 5e-4
-  lr_schedule: cosine_decay
-  batch_size: 4
-  gradient_accumulation: true
-  training_steps: 10000
-  precision: float32
+optimizer: AdamW
+learning_rate: 5e-4
+lr_schedule: cosine_decay
+batch_size: 4
+gradient_accumulation: true
+training_steps: 10000
+precision: float32
 
 # Intended Use
 intended_use:
-  primary_use_cases:
-    - Code completion and generation
-    - Function generation from descriptions
-    - Debugging assistance
-    - Code explanation and documentation
-    - Programming concept explanations
-    - Code scaffolding and prototyping
-  target_users:
-    - Software developers
-    - Students learning to code
-    - Data scientists
-    - DevOps engineers
-    - Technical writers
+primary_use_cases:
+- Code completion and generation
+- Function generation from descriptions
+- Debugging assistance
+- Code explanation and documentation
+- Programming concept explanations
+- Code scaffolding and prototyping
+target_users:
+- Software developers
+- Students learning to code
+- Data scientists
+- DevOps engineers
+- Technical writers
 
 # Limitations
 limitations:
-  - Limited to 1,024 token context window
-  - Trained primarily on Python code
-  - May generate code that requires review before production use
-  - Not suitable for non-coding tasks
+- Limited to 1,024 token context window
+- Trained primarily on Python code
+- May generate code that requires review before production use
+- Not suitable for non-coding tasks
 
 # Ethical Considerations
 ethical_considerations:
-  - Generated code should be reviewed before deployment
-  - May reproduce patterns from training data
-  - Not a replacement for human code review
-  - Users are responsible for compliance with licenses of generated code
+- Generated code should be reviewed before deployment
+- May reproduce patterns from training data
+- Not a replacement for human code review
+- Users are responsible for compliance with licenses of generated code
 
 # Citation
 citation: |
-  @misc{legioncoder2024,
-    title={Legion Coder 8M: A Compact Transformer for Code Generation},
-    author={DEATH LEGION},
-    year={2024},
-    howpublished={\url{https://huggingface.co/dineth554/legion-coder-8m}}
-  }
+@misc{legioncoder2026,
+title={Legion Coder 8M: A Compact Transformer for Code Generation},
+author={DEATH LEGION},
+year={2026},
+howpublished={\url{https://huggingface.co/dineth554/legion-coder-8m}}
+}
 
 # Contact
 contact:
-  developer: DEATH LEGION
-  powered_by: nvdya-kit
-  repository: https://huggingface.co/dineth554/legion-coder-8m
+developer: DEATH LEGION
+powered_by: nvdya-kit
+repository: https://huggingface.co/dineth554/legion-coder-8m
 
 # Branding
 branding:
-  tagline: MADE WITH BY DEATH LEGION
-  powered_by: nvdya-kit
-  copyright: © 2024 DEATH LEGION. All rights reserved.
----
+tagline: MADE WITH BY DEATH LEGION
+powered_by: nvdya-kit
+copyright: 2026 DEATH LEGION. All rights reserved.