Documentation
LLMITE Documentation
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Welcome to the LLMITE documentation. This section contains comprehensive guides and references for using LLMITE.
Getting Started
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API Reference
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Examples
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Getting Started with LLMITE
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This guide will help you get up and running with LLMITE quickly.
Prerequisites
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Before you begin, make sure you have:
- A modern web browser
- Basic understanding of web technologies
- Access to the LLMITE platform
Quick Setup
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Sign up for an account
- Visit the LLMITE homepage
- Click “Sign Up” and complete the registration process
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Verify your email
- Check your email for a verification link
- Click the link to activate your account
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Create your first project
Installation Guide
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Learn how to install and configure LLMITE for your specific environment.
System Requirements
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Minimum Requirements
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- 4GB RAM
- 2GB available disk space
- Internet connection
Recommended Requirements
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- 8GB RAM or more
- 5GB available disk space
- High-speed internet connection
Installation Methods
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Method 1: Quick Install
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curl -sSL https://get.llmite.com | bash
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This script will:
- Download the latest version
- Install dependencies
- Configure your environment
- Verify the installation
Method 2: Manual Installation
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Download the package
API Reference
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Complete reference for the LLMITE API.
Base URL
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All API requests should be made to:
https://api.llmite.com/v1
Authentication
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All API requests require authentication using an API key:
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curl -H "Authorization: Bearer YOUR_API_KEY" \
https://api.llmite.com/v1/endpoint
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Endpoints
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Projects
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List Projects
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Response:
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{
"projects": [
{
"id": "proj_123",
"name": "My Project",
"created_at": "2024-01-01T00:00:00Z",
"status": "active"
}
]
}
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Create Project
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Request Body:
Authentication
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Learn how to authenticate with the LLMITE API.
API Keys
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LLMITE uses API keys for authentication. You can manage your API keys from the dashboard.
Getting Your API Key
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- Log in to your LLMITE dashboard
- Navigate to “Settings” → “API Keys”
- Click “Generate New Key”
- Copy and securely store your key
**Security Notice**
Never share your API keys publicly or commit them to version control. Store them securely as environment variables.
Using API Keys
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Include your API key in the Authorization header:
Examples
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Practical examples to help you get started with LLMITE.
Basic Usage
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Simple Text Processing
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import requests
api_key = "sk-1234567890abcdef"
headers = {"Authorization": f"Bearer {api_key}"}
response = requests.post(
"https://api.llmite.com/v1/models/text-processor/execute",
headers=headers,
json={
"input": "Hello, world!",
"parameters": {
"temperature": 0.7
}
}
)
result = response.json()
print(result["output"])
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Project Management
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# List all projects
response = requests.get(
"https://api.llmite.com/v1/projects",
headers=headers
)
projects = response.json()["projects"]
# Create a new project
new_project = requests.post(
"https://api.llmite.com/v1/projects",
headers=headers,
json={
"name": "My New Project",
"description": "A sample project"
}
)
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JavaScript Examples
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Using Fetch API
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const apiKey = 'sk-1234567890abcdef';
async function processText(input) {
const response = await fetch('https://api.llmite.com/v1/models/text-processor/execute', {
method: 'POST',
headers: {
'Authorization': `Bearer ${apiKey}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
input: input,
parameters: {
temperature: 0.7,
max_tokens: 100
}
})
});
const result = await response.json();
return result.output;
}
// Usage
processText("Translate this to French: Hello world")
.then(output => console.log(output));
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Node.js with Express
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const express = require('express');
const axios = require('axios');
const app = express();
const apiKey = process.env.LLMITE_API_KEY;
app.use(express.json());
app.post('/process', async (req, res) => {
try {
const response = await axios.post(
'https://api.llmite.com/v1/models/text-processor/execute',
{
input: req.body.text,
parameters: req.body.parameters || {}
},
{
headers: {
'Authorization': `Bearer ${apiKey}`
}
}
);
res.json(response.data);
} catch (error) {
res.status(500).json({ error: error.message });
}
});
app.listen(3000, () => {
console.log('Server running on port 3000');
});
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Curl Examples
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Basic Request
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curl -X POST https://api.llmite.com/v1/models/text-processor/execute \
-H "Authorization: Bearer sk-1234567890abcdef" \
-H "Content-Type: application/json" \
-d '{
"input": "Summarize this text: Lorem ipsum dolor sit amet...",
"parameters": {
"temperature": 0.5,
"max_tokens": 50
}
}'
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Batch Processing
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curl -X POST https://api.llmite.com/v1/models/text-processor/batch \
-H "Authorization: Bearer sk-1234567890abcdef" \
-H "Content-Type: application/json" \
-d '{
"inputs": [
"First text to process",
"Second text to process",
"Third text to process"
],
"parameters": {
"temperature": 0.7
}
}'
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Error Handling
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Python with Error Handling
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import requests
from requests.exceptions import RequestException
def safe_api_call(endpoint, data):
try:
response = requests.post(
f"https://api.llmite.com/v1/{endpoint}",
headers={"Authorization": f"Bearer {api_key}"},
json=data,
timeout=30
)
response.raise_for_status()
return response.json()
except requests.exceptions.Timeout:
print("Request timed out")
except requests.exceptions.HTTPError as e:
print(f"HTTP error: {e.response.status_code}")
print(f"Error details: {e.response.json()}")
except RequestException as e:
print(f"Request failed: {e}")
return None
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Advanced Examples
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Streaming Responses
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import requests
import json
def stream_response(prompt):
response = requests.post(
"https://api.llmite.com/v1/models/text-processor/stream",
headers={"Authorization": f"Bearer {api_key}"},
json={"input": prompt, "stream": True},
stream=True
)
for line in response.iter_lines():
if line:
data = json.loads(line.decode('utf-8'))
if 'content' in data:
print(data['content'], end='', flush=True)
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Advanced Usage
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Advanced techniques and patterns for working with LLMITE.
Custom Models
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Training Your Own Model
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LLMITE allows you to train custom models using your own data:
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import llmite
client = llmite.Client(api_key="your-api-key")
# Upload training data
training_job = client.models.train(
name="my-custom-model",
training_data="path/to/training.jsonl",
validation_data="path/to/validation.jsonl",
parameters={
"learning_rate": 0.001,
"batch_size": 32,
"epochs": 10
}
)
# Monitor training progress
status = client.models.get_training_status(training_job.id)
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Fine-tuning Existing Models
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# Fine-tune a pre-trained model
fine_tune_job = client.models.fine_tune(
base_model="llmite-base-v1",
training_data="path/to/fine_tune_data.jsonl",
parameters={
"learning_rate": 0.0001,
"epochs": 3
}
)
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Batch Processing
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Large Scale Processing
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For processing large amounts of data efficiently: