ServerlessLLMChat
Qwen QwQ model focuses on advancing AI reasoning, and showcases the power of open models to match closed frontier model performance.QwQ-32B-Preview is an experimental release, comparable to o1 and surpassing GPT-4o and Claude 3.5 Sonnet on analytical and reasoning abilities across GPQA, AIME, MATH-500 and LiveCodeBench benchmarks. Note: This model is served experimentally as a serverless model. If you're deploying in production, be aware that Fireworks may undeploy the model with short notice.
Qwen Qwq 32b Preview 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 qwen-qwq-32b-preview. API reference
import requests import json url = "https://api.fireworks.ai/inference/v1/chat/completions" payload = { "model": "accounts/fireworks/models/qwen-qwq-32b-preview", "max_tokens": 4096, "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))
On-demand deployments allow you to use Qwen Qwq 32b Preview on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits.
See the On-demand deployments guide for details.