
Hugging Face Blog
路 1 min read
Welcome Fireworks.ai on the Hub 馃巻
Following our recent announcement on Inference Providers on the Hub, we're thrilled to share that Fireworks.ai is now a supported Inference Provider on HF Hub!
Fireworks.ai delivers blazing-fast serverless inference directly on model pages, as well as throughout the whole HF ecosystem of libraries and tools, making it easier than ever to run inference on your favorite models.
Among others, starting now, you can run serverless inference to the following models via Fireworks.ai:
- deepseek-ai/DeepSeek-R1
- deepseek-ai/DeepSeek-V3
- mistralai/Mistral-Small-24B-Instruct-2501
- Qwen/Qwen2.5-Coder-32B-Instruct
- meta-llama/Llama-3.2-90B-Vision-Instruct
and many more, you can find the full list here.
Light up your projects with Fireworks.ai today!
How it works
In the website UI
Search for all models supported by Fireworks on HF here.
From the client SDKs
from Python, using huggingface_hub
The following example shows how to use DeepSeek-R1 using Fireworks.ai as your inference provider. You can use a Hugging Face token for automatic routing through Hugging Face, or your own Fireworks.ai API key if you have one.
Install huggingface_hub from source:
pip install git+https://github.com/huggingface/huggingface_hub
Use the huggingface_hub python library to call Fireworks.ai endpoints by defining the provider parameter.
from huggingface_hub import InferenceClient
client = InferenceClient(
provider="fireworks-ai",
api_key="xxxxxxxxxxxxxxxxxxxxxxxx"
)
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
completion = client.chat.completions.create(
model="deepseek-ai/DeepSeek-R1",
messages=messages,
max_tokens=500
)
print(completion.choices[0].message)
from JS using @huggingface/inference
From HTTP calls
Here's how you can call Llama-3.3-70B-Instruct using Fireworks.ai as the inference provider via cURL.
Billing
For direct requests, i.e. when you use a Fireworks key, you are billed directly on your Fireworks account.
Original source
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