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
-
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.
-
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.
-
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.
-
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.
1from transformers import AutoTokenizer, T5ForConditionalGeneration
2
3model_name = "jrodriiguezg/grape-malbec"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = T5ForConditionalGeneration.from_pretrained(model_name)
6
7# Define the context (simulated environment)
8context = "Contexto: ['pwd=/home/user', 'ls=data.zip, notes.txt']"
9instruction = "despleiga un apache en el puerto 90"
10
11# Prepare input
12input_text = f"translate Spanish to Bash: {context} | {instruction}"
13input_ids = tokenizer(input_text, return_tensors="pt").input_ids
14
15# Generate command
16outputs = model.generate(input_ids, max_length=128)
17command = tokenizer.decode(outputs[0], skip_special_tokens=True)
18
19print(command)
20# Output: docker run -d -p 90:80 httpd
