ServerlessLLMTunableChat
DeepSeek R1 (Fast) is the speed-optimized serverless deployment of DeepSeek-R1. Compared to the DeepSeek R1 (Basic) endpoint, R1 (Fast) provides faster speeds with higher per-token prices, see https://fireworks.ai/pricing for details. Identical models are served on the two endpoints, so there are no quality or quantization differences. DeepSeek-R1 is a state-of-the-art large language model optimized with reinforcement learning and cold-start data for exceptional reasoning, math, and code performance. The model is identical to the one uploaded by DeepSeek on HuggingFace. Note that fine-tuning for this model is only available through contacting fireworks at https://fireworks.ai/company/contact-us.
DeepSeek R1 (Fast) 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 deepseek-r1. API reference
import requests import json url = "https://api.fireworks.ai/inference/v1/chat/completions" payload = { "model": "accounts/fireworks/models/deepseek-r1", "max_tokens": 20480, "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))
DeepSeek R1 (Fast) 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 DeepSeek R1 (Fast) on dedicated GPUs with Fireworks' high-performance serving stack with high reliability and no rate limits.
See the On-demand deployments guide for details.