| | --- |
| | language: |
| | - es |
| | tags: |
| | - docker |
| | - devops |
| | - cli |
| | - bash |
| | - text-to-code |
| | - grape-malbec |
| | license: apache-2.0 |
| | base_model: |
| | - Salesforce/codet5-small |
| | library_name: adapter-transformers |
| | datasets: |
| | - jrodriiguezg/malbec-nl2docker-es |
| | --- |
| | |
| | # Model Card for grape-malbec |
| |
|
| | **Model Name:** grape-malbec |
| | **Task:** Text-to-Text Generation (Natural Language to Docker) |
| | **Language:** Spanish (Input), Bash/Docker CLI (Output) |
| | **Domain:** DevOps, Containerization |
| | **Base Model:** Salesforce/codet5-small |
| |
|
| | ## Model Details |
| |
|
| | ### Model Description |
| | **grape-malbec** is a specialized language model designed to act as an intelligent Command Line Interface (CLI) assistant for Docker operations. It translates natural language instructions—often characterized by typos, technical slang, or ambiguity—into precise, safe, and context-aware Docker commands. |
| |
|
| | The model is engineered with a "safety-first" architecture, specifically trained to distinguish between informational queries and destructive actions to prevent accidental data loss in production environments. |
| |
|
| | * **Developed for:** DevOps Engineers, Developers, and CLI Tool Integrations. |
| | * **Model Type:** Fine-tuned Causal Language Model. |
| | * **Knowledge Domain:** Docker Container Lifecycle (Run, Stop, Remove, Exec, Logs, Network, Volume). |
| |
|
| | ## Uses |
| |
|
| | ### Direct Use |
| | The model is intended to be integrated into terminal emulators, shell plugins (like Zsh/Fish autosuggestions), or chat-based DevOps assistants. It takes a user's intent and the current directory context as input and outputs a single, executable shell command. |
| |
|
| | ### Key Capabilities |
| | 1. **Safety-Driven Intent Inference:** |
| | * Strictly differentiates between status checks (e.g., "cómo va", "estado") and destructive commands. A request for status never results in a `docker rm` command. |
| | * Distinguishes between creating a new container (`docker run`) and entering an existing one (`docker exec`) based on subtle linguistic cues. |
| |
|
| | 2. **Smart Port Mapping:** |
| | * Possesses knowledge of standard internal ports for popular services (e.g., 80 for Nginx, 3306 for MySQL). |
| | * If a user requests "Nginx en el puerto 90", the model correctly generates `-p 90:80`, mapping the custom host port to the correct standard container port. |
| |
|
| | 3. **Context Isolation:** |
| | * The model receives file system context (`pwd`, `ls`) but is trained to ignore irrelevant files (media, documents) and only utilize relevant files (like `Dockerfile` or `docker-compose.yml`) when necessary for the command. |
| |
|
| | 4. **Robustness to Noise:** |
| | * Highly tolerant of misspellings (e.g., "nginix", "posgres", "aranca") and informal verbs (e.g., "cárgate", "levanta", "tira"). |
| |
|
| | ## Examples |
| |
|
| | **Example 1: Smart Port Mapping (Mapeo Inteligente)** |
| | * **Input:** `Contexto: [] | despliega un apache en el puerto 90` |
| | * **Output:** `docker run -d -p 90:80 httpd` |
| |
|
| | **Example 2: Deployment Scenario (Despliegue)** |
| | * **Input:** `Contexto: [] | levanta un redis persistente llamado cache-prod` |
| | * **Output:** `docker run -d --name cache-prod -v redis_data:/data redis` |
| |
|
| | **Example 3: Safety correction (Status vs Remove)** |
| | * **Input:** `Contexto: [] | dime el estado del contenedor ubuntu` |
| | * **Output:** `docker ps -a --filter name=ubuntu` |
| | *(Note: Prevents generation of `docker rm` which can occur in untrained models due to token similarity in small datasets)* |
| | |
| | **Example 4: Run vs Exec (Ambigüedad Ejecutar)** |
| | * **Input:** `Contexto: [] | ejecuta un bash en el contenedor db` |
| | * **Output:** `docker exec -it db bash` |
| |
|
| | **Example 5: Typo Tolerance (Tolerancia a errores)** |
| | * **Input:** `Contexto: [] | stopea el contenedor nginix` |
| | * **Output:** `docker stop nginx` |
| |
|
| | ## How to Get Started |
| |
|
| | You can use this model with the transformers library. |
| | ```python |
| | from transformers import AutoTokenizer, T5ForConditionalGeneration |
| | |
| | model_name = "jrodriiguezg/grape-malbec" |
| | tokenizer = AutoTokenizer.from_pretrained(model_name) |
| | model = T5ForConditionalGeneration.from_pretrained(model_name) |
| | |
| | # Define the context (simulated environment) |
| | context = "Contexto: ['pwd=/home/user', 'ls=data.zip, notes.txt']" |
| | instruction = "despleiga un apache en el puerto 90" |
| | |
| | # Prepare input |
| | input_text = f"translate Spanish to Bash: {context} | {instruction}" |
| | input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
| | |
| | # Generate command |
| | outputs = model.generate(input_ids, max_length=128) |
| | command = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| | |
| | print(command) |
| | # Output: docker run -d -p 90:80 httpd |
| | ``` |
