Llama 3.2 3B Instruct
ServerlessLLMTunableChat
Llama 3.2 3B instruct is a lightweight, multilingual model from Meta. The model is designed for efficiency and offers substantial latency and cost improvements compared to larger models. Example use cases for the model include query and prompt rewriting and writing assistance
Serverless API
Llama 3.2 3B 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.
API Examples
Generate a model response using the chat endpoint of llama-v3p2-3b-instruct. API reference
import requests import json url = "https://api.fireworks.ai/inference/v1/chat/completions" payload = { "model": "accounts/fireworks/models/llama-v3p2-3b-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 3.2 3B 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
On-demand deployments allow you to use Llama 3.2 3B 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.