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Llama 3.3 70B Instruct

accounts/fireworks/models/llama-v3p3-70b-instruct

ServerlessLLMTunableChat

Llama 3.3 70B Instruct is the December update of Llama 3.1 70B. The model improves upon Llama 3.1 70B (released July 2024) with advances in tool calling, multilingual text support, math and coding. The model achieves industry leading results in reasoning, math and instruction following and provides similar performance as 3.1 405B but with significant speed and cost improvements.

Serverless API

Llama 3.3 70B 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.

Try it

API Examples

Generate a model response using the chat endpoint of llama-v3p3-70b-instruct. API reference

import requests
import json

url = "https://api.fireworks.ai/inference/v1/chat/completions"
payload = {
  "model": "accounts/fireworks/models/llama-v3p3-70b-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))

Fine-tuning

Llama 3.3 70B 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.

Fine-tune this model

On-demand deployments

On-demand deployments allow you to use Llama 3.3 70B 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.

Deploy this model