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Qubrid AI
Microsoft ยท Chat / LLM ยท 7B Parameters ยท 8K ContextQubrid Playground License HuggingFaceStreaming Instruction Following Code Chat

Overview

Fara 7B is a compact and efficient transformer model developed by Microsoft for high-speed inference, instruction following, text generation, and lightweight reasoning tasks. Its small parameter size allows easy deployment on consumer GPUs and edge devices while maintaining strong performance. Whether youโ€™re building customer-facing assistants, content pipelines, or developer tooling, Fara 7B delivers reliable, low-latency responses at a fraction of the cost of larger models.
๐ŸŽ๏ธ Runs on consumer GPUs and edge devices โ€” fast, lightweight, and production-ready. Deploy via the Qubrid AI Serverless API for just $0.21 / 1M input tokens.

Model Specifications

FieldDetails
Model IDmicrosoft/Fara-7B
ProviderMicrosoft
KindChat / LLM
ArchitectureDecoder-only Transformer
Parameters7B
Context Length8,192 Tokens
MoENo
Release Date2025
LicenseMIT
Training DataMixed web, curated instructional datasets, code, and multilingual corpora
Function CallingNot Supported
Image SupportN/A
Serverless APIAvailable
Fine-tuningComing Soon
On-demandComing Soon
State๐ŸŸข Ready

Pricing

๐Ÿ’ณ Access via the Qubrid AI Serverless API with pay-per-token pricing. No infrastructure management required.
Token TypePrice per 1M Tokens
Input Tokens$0.21
Output Tokens$0.25

Quickstart

Prerequisites

  1. Create a free account at platform.qubrid.com
  2. Generate your API key from the API Keys section
  3. Replace QUBRID_API_KEY in the code below with your actual key

Python

from openai import OpenAI

# Initialize the OpenAI client with Qubrid base URL
client = OpenAI(
  base_url="https://platform.qubrid.com/v1",
  api_key="QUBRID_API_KEY",
)

# Create a streaming chat completion
stream = client.chat.completions.create(
  model="microsoft/Fara-7B",
  messages=[
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  max_tokens=4096,
  temperature=0.7,
  top_p=1,
  stream=True
)

# If stream = False comment this out
for chunk in stream:
  if chunk.choices and chunk.choices[0].delta.content:
      print(chunk.choices[0].delta.content, end="", flush=True)
print("\n")

# If stream = True comment this out
print(stream.choices[0].message.content)

JavaScript

import OpenAI from 'openai';

// Initialize the OpenAI client with Qubrid base URL
const client = new OpenAI({
  baseURL: 'https://platform.qubrid.com/v1',
  apiKey: 'QUBRID_API_KEY',
});

// Create a streaming chat completion
const stream = await client.chat.completions.create({
  model: 'microsoft/Fara-7B',
  messages: [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  max_tokens: 4096,
  temperature: 0.7,
  top_p: 1,
  stream: true
});

// If stream = false comment this out
for await (const chunk of stream) {
  if (chunk.choices[0]?.delta?.content) {
    process.stdout.write(chunk.choices[0].delta.content);
  }
}
console.log('\n');

// If stream = true comment this out
console.log(stream.choices[0].message.content);

Go

package main

import (
	"bufio"
	"bytes"
	"encoding/json"
	"fmt"
	"net/http"
)

func main() {
	url := "https://platform.qubrid.com/v1/chat/completions"

	data := map[string]interface{}{
		"model": "microsoft/Fara-7B",
		"messages": []map[string]string{
			{
				"role":    "user",
				"content": "Explain quantum computing in simple terms",
			},
		},
		"temperature": 0.7,
		"max_tokens":  4096,
		"stream":      true,
		"top_p":       1,
	}

	jsonData, _ := json.Marshal(data)
	req, _ := http.NewRequest("POST", url, bytes.NewBuffer(jsonData))
	req.Header.Set("Authorization", "Bearer QUBRID_API_KEY")
	req.Header.Set("Content-Type", "application/json")

	client := &http.Client{}
	res, _ := client.Do(req)
	defer res.Body.Close()

	scanner := bufio.NewScanner(res.Body)
	for scanner.Scan() {
		line := scanner.Text()
		if line != "" {
			fmt.Println(line)
		}
	}
}

cURL

curl -X POST "https://platform.qubrid.com/v1/chat/completions" \
  -H "Authorization: Bearer QUBRID_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "microsoft/Fara-7B",
  "messages": [
    {
      "role": "user",
      "content": "Explain quantum computing in simple terms"
    }
  ],
  "temperature": 0.7,
  "max_tokens": 4096,
  "stream": true,
  "top_p": 1
}'

Live Example

Prompt: Explain quantum computing in simple terms
Response:
Quantum computing uses quantum mechanics to process information differently
from classical computers.

Classical computers use bits โ€” each is a 0 or a 1.
Quantum computers use qubits, which can be 0, 1, or both at once
(called superposition).

This means a quantum computer can explore many solutions at the same time,
rather than checking them one by one โ€” making it exponentially faster
for certain types of problems:

  - Encryption and cybersecurity
  - Simulating molecular structures for drug discovery
  - Optimization in finance and logistics

Another key property: entanglement โ€” two qubits can be linked so that
knowing the state of one instantly tells you something about the other,
enabling highly coordinated computation at scale.

Quantum computers aren't replacing laptops anytime soon.
They're a precision tool for problems that would take classical machines
millions of years to solve.
Try it yourself in the Qubrid AI Playground โ†’

Playground Features

The Qubrid AI Playground lets you chat with Fara 7B directly in your browser โ€” no setup, no code, no cost to explore.

๐Ÿง  System Prompt

Set the modelโ€™s role, tone, and boundaries before the conversation begins. Perfect for focused assistants and domain-specific bots โ€” without touching any code.
Example: "You are a helpful product assistant for an e-commerce store.
Only answer questions about products, orders, and returns.
Always respond in a friendly, concise tone."
Set your system prompt once in the Qubrid Playground and it applies across every turn of the conversation.

๐ŸŽฏ Few-Shot Examples

Show the model exactly what good output looks like โ€” before your real query. No fine-tuning, no retraining required.
User InputAssistant Response
Write a product description for wireless headphonesExperience music like never before. These wireless headphones deliver rich, immersive sound with up to 30 hours of battery life โ€” so you can keep going, even when the playlist doesn't stop.
Summarize this support ticket in one lineCustomer is unable to log in due to a forgotten password and is requesting a reset link.
๐Ÿ’ก Add few-shot examples directly in the Qubrid Playground to dial in tone, format, and domain focus โ€” no fine-tuning required.

Inference Parameters

ParameterTypeDefaultDescription
StreamingbooleantrueEnable streaming responses for real-time output
Temperaturenumber0.7Controls creativity and randomness. Higher values produce more diverse output
Max Tokensnumber4096Maximum number of tokens the model can generate
Top Pnumber1Nucleus sampling: restricts token selection to a probability mass threshold

Use Cases

  1. Customer-facing chatbots and virtual assistants that handle FAQs and multi-turn dialogue
  2. Long-form and short-form content generation such as blogs, emails, and product descriptions
  3. Developer code assistance for completion, explanation, and small refactors
  4. General question answering over product, documentation, or knowledge-base content
  5. Summarization of long documents, transcripts, and knowledge-dense articles

Strengths & Limitations

StrengthsLimitations
Runs efficiently on consumer and cloud GPUsLower reasoning capability than larger models (30Bโ€“120B)
Strong instruction-following capability for a 7B modelLimited long-context performance (8K window)
Optimized for low-latency inferenceMay require fine-tuning for specialized domain tasks
Open weights allow on-prem and edge deploymentFunction calling not supported

Why Qubrid AI?

  • ๐Ÿš€ No infrastructure setup โ€” serverless API, pay only for what you use
  • ๐Ÿ” OpenAI-compatible โ€” drop-in replacement using the same SDK, just swap the base URL
  • โšก Edge-optimized serving โ€” Fara 7Bโ€™s compact footprint meets Qubridโ€™s low-latency infrastructure
  • ๐Ÿงช Built-in Playground โ€” prototype with system prompts and few-shot examples instantly at platform.qubrid.com
  • ๐Ÿ“Š Full observability โ€” API logs and usage tracking built into the Qubrid dashboard
  • ๐ŸŒ Multi-language support โ€” Python, JavaScript, Go, cURL out of the box

Resources

ResourceLink
๐Ÿ“– Qubrid Docsdocs.platform.qubrid.com
๐ŸŽฎ PlaygroundTry Fara 7B live
๐Ÿ”‘ API KeysGet your API Key
๐Ÿค— Hugging Facemicrosoft/Fara-7B
๐Ÿ’ฌ DiscordJoin the Qubrid Community

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