ServerlessLLMTunableChat
Dobby-Unhinged-Llama-3.3-70B is the first ever Loyal model. It is owned and controlled by ~700,000 people, functions only in alignment with pro-freedom and pro-crypto values as the other Dobby models, all the while maintaining the strong general capabilities from Llama-3.3-70B. The Dobby models have their own unique personalities and are blunt, a bit rude, and often controversial. Given its AI friend tone and high performance marks, Dobby is prime to power anything from viral social and companion agents to utility chatbots with strong attitudes.
Dobby-Unhinged-Llama-3.3-70B 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 dobby-unhinged-llama-3-3-70b-new. API reference
import requests import json url = "https://api.fireworks.ai/inference/v1/chat/completions" payload = { "model": "accounts/sentientfoundation/models/dobby-unhinged-llama-3-3-70b-new", "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))
Dobby-Unhinged-Llama-3.3-70B 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 Dobby-Unhinged-Llama-3.3-70B on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits.
See the On-demand deployments guide for details.