ServerlessLLMTunableChat
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes. The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks. 405B model is the most capable from the Llama 3.1 family. This model is served in FP8 closely matching reference implementation.
Llama 3.1 405B Instruct is available via Fireworks' serverless API, where you pay per token. There are several ways to call the Fireworks API, including Fireworks' Python client, the REST API, or OpenAI's Python client.
See below for easy generation of calls and a description of the raw REST API for making API requests. See the Querying text models docs for details.
Generate a model response using the chat endpoint of llama-v3p1-405b-instruct. API reference
import requests import json url = "https://api.fireworks.ai/inference/v1/chat/completions" payload = { "model": "accounts/fireworks/models/llama-v3p1-405b-instruct", "max_tokens": 16384, "top_p": 1, "top_k": 40, "presence_penalty": 0, "frequency_penalty": 0, "temperature": 0.6, "messages": [ { "role": "user", "content": "Hello, how are you?" } ] } headers = { "Accept": "application/json", "Content-Type": "application/json", "Authorization": "Bearer <API_KEY>" } requests.request("POST", url, headers=headers, data=json.dumps(payload))
Llama 3.1 405B Instruct can be fine-tuned on your data to create a model with better response quality. Fireworks uses low-rank adaptation (LoRA) to train a model that can be served efficiently at inference time.
See the Fine-tuning guide for details.
On-demand deployments allow you to use Llama 3.1 405B Instruct on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits.
See the On-demand deployments guide for details.