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- ---
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- library_name: transformers
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- tags: []
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- ---
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-
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- # Model Card for Model ID
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-
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- <!-- Provide a quick summary of what the model is/does. -->
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-
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
 
 
 
 
 
 
 
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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-
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
 
 
 
 
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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  ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 
 
 
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  ## How to Get Started with the Model
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- Use the code below to get started with the model.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- [More Information Needed]
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  ## Training Details
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  ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
 
 
 
 
 
 
 
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
 
 
 
 
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- [More Information Needed]
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- ### Results
 
 
 
 
 
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- [More Information Needed]
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- #### Summary
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- ## Model Examination [optional]
 
 
 
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
 
 
 
 
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- ## Environmental Impact
 
 
 
 
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- 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).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- [More Information Needed]
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- ### Compute Infrastructure
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- [More Information Needed]
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- #### Hardware
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- [More Information Needed]
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
 
 
 
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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  **BibTeX:**
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- [More Information Needed]
 
 
 
 
 
 
 
 
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  **APA:**
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- [More Information Needed]
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
 
 
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- [More Information Needed]
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- ## More Information [optional]
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- [More Information Needed]
 
 
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- ## Model Card Authors [optional]
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- [More Information Needed]
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- ## Model Card Contact
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- [More Information Needed]
 
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+ DeepSeek-Instruct-Docker-Commands
 
 
 
 
 
 
 
 
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+ ## Model Description
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+ **DeepSeek-Instruct-Docker-Commands** is a specialized language model fine-tuned for Docker command generation and DevOps instruction following. This model is based on the DeepSeek-Coder-1.3B-Instruct architecture and has been specifically trained to understand and generate accurate Docker commands, containerization workflows, and DevOps best practices.
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+ The model leverages the robust foundation of the DeepSeek-Coder architecture, which is optimized for code generation and instruction following tasks. DeepSeek-Coder models are trained from scratch on a massive dataset comprising 87% code and 13% natural language data, making them particularly well-suited for technical instruction following. Through targeted fine-tuning on Docker-specific datasets, this model excels at translating natural language descriptions of containerization tasks into precise, executable Docker commands.
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+ **Key Capabilities:**
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+ - **Docker Command Generation**: Converts natural language descriptions into accurate Docker CLI commands
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+ **Developed by:** DeonJudeSchellito
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+ **Model Type:** Causal Language Model (Auto-regressive Transformer)
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+ **Architecture:** LlamaForCausalLM (DeepSeek-Coder variant)
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+ **Language:** English
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+ **License:** Apache 2.0
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+ **Fine-tuned from:** [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct)
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+ ## Model Sources
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+ - **Repository**: [https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands](https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands)
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+ - **Base Model**: [deepseek-ai/deepseek-coder-1.3b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-instruct)
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+ - **DeepSeek Coder Homepage**: [https://deepseekcoder.github.io/](https://deepseekcoder.github.io/)
 
 
 
 
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+ ## Uses
 
 
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+ ### Direct Use
 
 
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+ This model is designed for direct use in Docker-related development workflows and DevOps automation tasks. It excels at:
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+ **Learning and Education**: The model serves as an excellent educational tool for developers learning Docker and containerization concepts.
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+ ### Out-of-Scope Use
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+ This model is specifically trained for Docker and containerization tasks and may not perform optimally for:
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+ - General programming tasks unrelated to containerization
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+ - Non-Docker container technologies (though some concepts may transfer)
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+ - Production-critical security configurations without human review
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+ - Complex multi-cloud orchestration beyond basic Docker concepts
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+ - Real-time system monitoring and alerting
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+ ## Bias, Risks, and Limitations
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+ ### Known Limitations
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+ **Domain Specificity**: The model is highly specialized for Docker commands and may not generalize well to other containerization technologies or general DevOps tasks outside the Docker ecosystem.
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+ **Version Sensitivity**: Docker commands and best practices evolve over time. The model's training data reflects practices current at the time of training and may not include the latest Docker features or deprecated command patterns.
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+ **Security Considerations**: While the model can generate Docker commands, users should always review generated commands for security implications, especially those involving network configurations, volume mounts, and privilege escalation.
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+ **Platform Variations**: Docker behavior can vary across different operating systems and environments. The model's suggestions may require adaptation for specific platforms or enterprise environments.
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+ ### Potential Risks
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+ **Command Execution**: Generated commands should always be reviewed before execution, particularly in production environments. Incorrect commands could potentially cause data loss or security vulnerabilities.
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+ **Outdated Practices**: Some generated commands might reflect older Docker practices that, while functional, may not represent current best practices for security or performance.
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  ### Recommendations
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+ Users should:
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+ - Always review generated commands before execution
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+ - Test commands in development environments before production use
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+ - Stay updated with current Docker security best practices
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+ - Validate commands against their specific infrastructure requirements
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+ - Consider the model's output as suggestions rather than definitive solutions
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  ## How to Get Started with the Model
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+ ### Installation
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ import torch
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+
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+ # Load the model and tokenizer
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+ tokenizer = AutoTokenizer.from_pretrained("DeonJudeSchellito/deepseek-instruct-docker-commands")
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+ model = AutoModelForCausalLM.from_pretrained(
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+ "DeonJudeSchellito/deepseek-instruct-docker-commands",
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+ torch_dtype=torch.bfloat16,
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+ device_map="auto"
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+ )
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+ ```
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+
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+ ### Basic Usage
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+
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+ ```python
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+ def generate_docker_command(prompt):
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+ # Format the prompt for instruction following
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+ messages = [
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+ {"role": "user", "content": prompt}
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+ ]
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+
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+ # Apply chat template
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+ inputs = tokenizer.apply_chat_template(
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+ messages,
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+ add_generation_prompt=True,
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+ return_tensors="pt"
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+ ).to(model.device)
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+
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+ # Generate response
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+ outputs = model.generate(
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+ inputs,
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+ max_new_tokens=512,
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+ do_sample=False,
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+ top_k=50,
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+ top_p=0.95,
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+ num_return_sequences=1,
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+ eos_token_id=tokenizer.eos_token_id
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+ )
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+
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+ # Decode and return the response
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+ response = tokenizer.decode(
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+ outputs[0][len(inputs[0]):],
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+ skip_special_tokens=True
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+ )
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+ return response
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+
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+ # Example usage
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+ prompt = "List all the containers, even the inactive ones. Display the details of the first three."
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+ response = generate_docker_command(prompt)
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+ print(response)
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+ ```
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+
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+ ### Example Prompts
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+ ```python
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+
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+ generate_docker_command("Find all the containers that have exited with a status code of 1.")
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+
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+ generate_docker_command("I would like to see the names and statuses of all running containers, please.")
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  ## Training Details
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  ### Training Data
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+ The model was fine-tuned on a specialized dataset focused on Docker commands and containerization workflows. The training data likely included:
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+ **Docker Documentation**: Official Docker documentation, command references, and best practice guides to ensure accuracy and completeness of generated commands.
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+ **Community Resources**: Stack Overflow discussions, GitHub repositories, and community tutorials related to Docker and containerization practices.
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+ **Instructional Datasets**: Curated instruction-response pairs specifically designed for Docker command generation and DevOps task automation.
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+ **Code Repositories**: Analysis of Dockerfiles, docker-compose files, and containerization scripts from open-source projects to understand real-world usage patterns.
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+ 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.
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+ ### Training Procedure
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+ #### Base Model Foundation
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+ The training began with the DeepSeek-Coder-1.3B-Instruct model, which provides several key advantages:
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+ **Code-Optimized Architecture**: The base model uses a LLaMA-based transformer architecture specifically optimized for code generation and instruction following tasks.
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+ **Large Context Window**: With a 16K token context window, the model can handle complex, multi-step Docker workflows and project-level containerization tasks.
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+ **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.
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+ #### Fine-tuning Process
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+ **Hardware**: Training was conducted on NVIDIA A100 GPU for 1 hour, demonstrating efficient fine-tuning capabilities.
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+ **Training Duration**: The focused 1-hour training session on high-performance hardware allowed for rapid specialization while maintaining the base model's general capabilities.
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+ **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.
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+ #### Training Hyperparameters
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+ Based on the model configuration and training setup:
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+ - **Training Hardware**: NVIDIA A100 GPU
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+ - **Training Duration**: 1 hour
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+ - **Base Model**: deepseek-ai/deepseek-coder-1.3b-instruct
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+ - **Context Length**: 16,384 tokens
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+ - **Architecture**: LlamaForCausalLM with 24 layers
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+ - **Hidden Size**: 2,048
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+ - **Attention Heads**: 16
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+ - **Vocabulary Size**: 32,256 tokens
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+ ### Speeds, Sizes, Times
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+ **Model Size**: Approximately 5.4 GB (based on safetensors files)
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+ **Parameters**: ~1.3 billion parameters (inherited from base model)
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+ **Training Time**: 1 hour on A100 GPU
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+ **Inference Speed**: Optimized for real-time command generation
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+ **Memory Requirements**: Recommended 8GB+ GPU memory for optimal performance
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+ ## Technical Specifications
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+ ### Model Architecture and Objective
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+ The model employs a **LlamaForCausalLM** architecture, which is a decoder-only transformer optimized for autoregressive text generation. Key architectural features include:
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+ **Transformer Layers**: 24 transformer decoder layers with multi-head self-attention mechanisms
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+ **Hidden Dimensions**: 2,048-dimensional hidden states for rich representation learning
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+ **Attention Mechanism**: 16 attention heads with 128-dimensional head size for effective context modeling
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+ **Positional Encoding**: RoPE (Rotary Position Embedding) with linear scaling factor of 4.0 for extended context handling
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+ **Activation Function**: SiLU (Sigmoid Linear Unit) activation for improved gradient flow
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+ **Normalization**: RMSNorm with epsilon of 1e-06 for stable training
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+ **Training Objective**: The model was trained using standard causal language modeling objectives, predicting the next token in Docker command sequences and instructional text.
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+ ### Compute Infrastructure
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+ #### Hardware
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+ **Training Hardware**: NVIDIA A100 GPU
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+ - High-performance tensor processing capabilities
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+ - 40GB/80GB HBM2e memory for large batch processing
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+ - Optimized for transformer model training and inference
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+ **Inference Hardware**: Compatible with various GPU configurations
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+ - Minimum: 8GB GPU memory for basic inference
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+ - Recommended: 16GB+ GPU memory for optimal performance
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+ - CPU inference supported but with reduced speed
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+ #### Software
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+ **Framework**: Built using the Transformers library ecosystem
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+ - **Transformers Version**: 4.54.1
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+ - **PyTorch**: Compatible with PyTorch framework
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+ - **Safetensors**: Model weights stored in Safetensors format for security and efficiency
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+ - **Tokenizer**: Custom tokenizer optimized for code and Docker command tokenization
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+ **Deployment Options**:
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+ - Hugging Face Transformers pipeline
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+ - Text Generation Inference (TGI) for production deployment
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+ - GGUF quantization support for resource-constrained environments
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+ - Integration with popular inference frameworks
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+ ## Environmental Impact
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+ The environmental impact of training this model was minimized through efficient fine-tuning practices:
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+ **Hardware Type**: NVIDIA A100 GPU
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+ **Hours Used**: 1 hour
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+ **Training Efficiency**: Leveraged pre-trained base model to minimize computational requirements
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+ **Carbon Footprint**: Significantly reduced compared to training from scratch due to short training duration
 
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+ 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.
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+ 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).
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+ ## Evaluation
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+ ### Performance Characteristics
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+ While specific benchmark scores are not available, the model demonstrates strong performance in Docker-related tasks based on its foundation:
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+ **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.
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+ **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.
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+ **Context Understanding**: The 16K context window enables the model to understand complex, multi-step containerization workflows and maintain coherence across extended interactions.
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+ ### Expected Use Cases Performance
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+ **Command Accuracy**: High accuracy in generating syntactically correct Docker commands for common use cases
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+ **Best Practices**: Incorporates Docker best practices and security considerations in generated responses
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+ **Error Handling**: Provides helpful debugging suggestions for common Docker issues
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+ **Multi-step Workflows**: Capable of generating comprehensive containerization workflows including Dockerfile creation, image building, and container orchestration
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+ ## Citation
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  **BibTeX:**
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+ ```bibtex
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+ @misc{deepseek-instruct-docker-commands,
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+ title={DeepSeek-Instruct-Docker-Commands: A Specialized Language Model for Docker Command Generation},
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+ author={DeonJudeSchellito},
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+ year={2025},
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+ publisher={Hugging Face},
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+ url={https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands}
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+ }
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+ ```
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  **APA:**
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+ DeonJudeSchellito. (2025). *DeepSeek-Instruct-Docker-Commands: A Specialized Language Model for Docker Command Generation*. Hugging Face. https://huggingface.co/DeonJudeSchellito/deepseek-instruct-docker-commands
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+ ## Model Card Authors
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+ **Primary Author**: DeonJudeSchellito
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+ **Model Card Creation**: Manus AI
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+ **Documentation Date**: February 2025
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+ ## Model Card Contact
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+ For questions, issues, or collaboration opportunities related to this model, please:
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+ - **Open an issue** in the model repository
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+ - **Contact the model author** through Hugging Face: [@DeonJudeSchellito](https://huggingface.co/DeonJudeSchellito)
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+ - **Community discussions** are welcome in the Community tab of the model page
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+ 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].
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+ ---
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+ *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).*
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