DeonJudeSchellito
/

deepseek-instruct-docker-commands

@@ -131,174 +131,3 @@ generate_docker_command("Find all the containers that have exited with a status
 generate_docker_command("I would like to see the names and statuses of all running containers, please.")
-## Training Details
-### Training Data
-The model was fine-tuned on a specialized dataset focused on Docker commands and containerization workflows. The training data likely included:
-**Docker Documentation**: Official Docker documentation, command references, and best practice guides to ensure accuracy and completeness of generated commands.
-**Community Resources**: Stack Overflow discussions, GitHub repositories, and community tutorials related to Docker and containerization practices.
-**Instructional Datasets**: Curated instruction-response pairs specifically designed for Docker command generation and DevOps task automation.
-**Code Repositories**: Analysis of Dockerfiles, docker-compose files, and containerization scripts from open-source projects to understand real-world usage patterns.
-The training process built upon the strong foundation of the DeepSeek-Coder-1.3B-Instruct base model, which was originally trained on 2 trillion tokens comprising 87% code and 13% natural language data in English and Chinese.
-### Training Procedure
-#### Base Model Foundation
-The training began with the DeepSeek-Coder-1.3B-Instruct model, which provides several key advantages:
-**Code-Optimized Architecture**: The base model uses a LLaMA-based transformer architecture specifically optimized for code generation and instruction following tasks.
-**Large Context Window**: With a 16K token context window, the model can handle complex, multi-step Docker workflows and project-level containerization tasks.
-**Instruction Tuning**: The base model was already fine-tuned on 2 billion tokens of instruction data, providing a strong foundation for following Docker-related instructions.
-#### Fine-tuning Process
-**Hardware**: Training was conducted on NVIDIA A100 GPU for 1 hour, demonstrating efficient fine-tuning capabilities.
-**Training Duration**: The focused 1-hour training session on high-performance hardware allowed for rapid specialization while maintaining the base model's general capabilities.
-**Optimization Strategy**: The training likely employed parameter-efficient fine-tuning techniques to specialize the model for Docker tasks while preserving the underlying code generation capabilities.
-#### Training Hyperparameters
-Based on the model configuration and training setup:
-- **Training Hardware**: NVIDIA A100 GPU
-- **Training Duration**: 1 hour
-- **Base Model**: deepseek-ai/deepseek-coder-1.3b-instruct
-- **Context Length**: 16,384 tokens
-- **Architecture**: LlamaForCausalLM with 24 layers
-- **Hidden Size**: 2,048
-- **Attention Heads**: 16
-- **Vocabulary Size**: 32,256 tokens
-### Speeds, Sizes, Times
-**Model Size**: Approximately 5.4 GB (based on safetensors files)
-**Parameters**: ~1.3 billion parameters (inherited from base model)
-**Training Time**: 1 hour on A100 GPU
-**Inference Speed**: Optimized for real-time command generation
-**Memory Requirements**: Recommended 8GB+ GPU memory for optimal performance
-## Technical Specifications
-### Model Architecture and Objective
-The model employs a **LlamaForCausalLM** architecture, which is a decoder-only transformer optimized for autoregressive text generation. Key architectural features include:
-**Transformer Layers**: 24 transformer decoder layers with multi-head self-attention mechanisms
-**Hidden Dimensions**: 2,048-dimensional hidden states for rich representation learning
-**Attention Mechanism**: 16 attention heads with 128-dimensional head size for effective context modeling
-**Positional Encoding**: RoPE (Rotary Position Embedding) with linear scaling factor of 4.0 for extended context handling
-**Activation Function**: SiLU (Sigmoid Linear Unit) activation for improved gradient flow
-**Normalization**: RMSNorm with epsilon of 1e-06 for stable training
-**Training Objective**: The model was trained using standard causal language modeling objectives, predicting the next token in Docker command sequences and instructional text.
-### Compute Infrastructure
-#### Hardware
-**Training Hardware**: NVIDIA A100 GPU
-- High-performance tensor processing capabilities
-- 40GB/80GB HBM2e memory for large batch processing
-- Optimized for transformer model training and inference
-**Inference Hardware**: Compatible with various GPU configurations
-- Minimum: 8GB GPU memory for basic inference
-- Recommended: 16GB+ GPU memory for optimal performance
-- CPU inference supported but with reduced speed
-#### Software
-**Framework**: Built using the Transformers library ecosystem
-- **Transformers Version**: 4.54.1
-- **PyTorch**: Compatible with PyTorch framework
-- **Safetensors**: Model weights stored in Safetensors format for security and efficiency
-- **Tokenizer**: Custom tokenizer optimized for code and Docker command tokenization
-**Deployment Options**:
-- Hugging Face Transformers pipeline
-- Text Generation Inference (TGI) for production deployment
-- GGUF quantization support for resource-constrained environments
-- Integration with popular inference frameworks
-## Environmental Impact
-The environmental impact of training this model was minimized through efficient fine-tuning practices:
-**Hardware Type**: NVIDIA A100 GPU
-**Hours Used**: 1 hour
-**Training Efficiency**: Leveraged pre-trained base model to minimize computational requirements
-**Carbon Footprint**: Significantly reduced compared to training from scratch due to short training duration
-The brief training period demonstrates the efficiency of fine-tuning specialized models from strong base models, reducing both computational costs and environmental impact while achieving targeted performance improvements.
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-## Evaluation
-### Performance Characteristics
-While specific benchmark scores are not available, the model demonstrates strong performance in Docker-related tasks based on its foundation:
-**Base Model Performance**: The DeepSeek-Coder-1.3B-Instruct base model achieves state-of-the-art performance among open-source code models on multiple programming benchmarks including HumanEval, MultiPL-E, MBPP, DS-1000, and APPS.
-**Specialization Benefits**: Fine-tuning on Docker-specific data enhances the model's ability to generate accurate, executable Docker commands while maintaining the base model's strong code generation capabilities.
-**Context Understanding**: The 16K context window enables the model to understand complex, multi-step containerization workflows and maintain coherence across extended interactions.
-### Expected Use Cases Performance
-**Command Accuracy**: High accuracy in generating syntactically correct Docker commands for common use cases
-**Best Practices**: Incorporates Docker best practices and security considerations in generated responses
-**Error Handling**: Provides helpful debugging suggestions for common Docker issues
-**Multi-step Workflows**: Capable of generating comprehensive containerization workflows including Dockerfile creation, image building, and container orchestration
-## Citation
-**BibTeX:**
-```bibtex
-@misc{deepseek-instruct-docker-commands,
-  title={DeepSeek-Instruct-Docker-Commands: A Specialized Language Model for Docker Command Generation},
-  author={DeonJudeSchellito},
-  year={2025},
-  publisher={Hugging Face},
-  url={https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands}
-}
-```
-**APA:**
-DeonJudeSchellito. (2025). *DeepSeek-Instruct-Docker-Commands: A Specialized Language Model for Docker Command Generation*. Hugging Face. https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands
-## Model Card Authors
-**Primary Author**: DeonJudeSchellito
-**Model Card Creation**: Manus AI
-**Documentation Date**: February 2025
-## Model Card Contact
-For questions, issues, or collaboration opportunities related to this model, please:
-- **Open an issue** in the model repository
-- **Contact the model author** through Hugging Face: [@DeonJudeSchellito](https://huggingface.co/DeonJudeSchellito)
-- **Community discussions** are welcome in the Community tab of the model page
-For technical support or questions about the base DeepSeek-Coder model, refer to the [official DeepSeek repository](https://github.com/deepseek-ai/DeepSeek-Coder) or contact [email protected].
----
-*This model card was generated to provide comprehensive information about the DeepSeek-Instruct-Docker-Commands model. For the most up-to-date information and model files, please visit the [official model page](https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands).*


131
132	generate_docker_command("I would like to see the names and statuses of all running containers, please.")
133