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Llama 3.1 8B Instruct

Llama-3.1-8B-Instruct is an 8B-parameter multilingual chat and instruction-following model from Meta with a 128k context window, strong tool usage, and efficient performance for real-time assistants.
Core Model
Chat

How to get started 

pip install requestsCode language: Bash (bash)
import requests


api_url = "https://api.regolo.ai/v1/chat/completions"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_REGOLO_KEY"
}
data = {
  "model": "Llama-3.1-8B-Instruct",
  "messages": [
    {
      "role": "user",
      "content": "What is the capital of Italy, and which region does it belong to?"
    }
  ]
}

response = requests.post(api_url, headers=headers, json=data)
print(response.json())Code language: Python (python)

Applications & Use Cases

  • Multilingual chat assistants for customer support, internal help desks, and knowledge bots across English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
  • Coding and DevOps copilots that generate, refactor, and explain code in editor integrations or CLI tools, using the instruct tuning for precise task execution.
  • RAG-based enterprise copilots that answer questions over documentation, wikis, and tickets while leveraging the 128k context for long documents and rich conversation history.
  • Tool and function-calling agents that orchestrate workflows such as ticket triage, reporting, monitoring, and simple transactional flows via structured outputs.
  • Content generation systems for marketing, product documentation, and localization pipelines that need fluent, controllable text in multiple languages.
  • Evaluation, guardrail, and alignment layers where a smaller, fast open-weight model is used to critique, rank, or filter generations from larger models