Hyperbolic

Overview

<br /> <br />

https://docs.hyperbolic.xyz

We support ALL Hyperbolic models, just set hyperbolic/ as a prefix when sending completion requests

Available Models

Language Models

Required Variables

os.environ["HYPERBOLIC_API_KEY"] = ""  # your Hyperbolic API key

Get your API key from Hyperbolic dashboard.

Usage - LiteLLM Python SDK

Non-streaming

from litellm import completion

os.environ["HYPERBOLIC_API_KEY"] = ""  # your Hyperbolic API key

messages = [{"content": "What is the capital of France?", "role": "user"}]

# Hyperbolic call
response = completion(
    model="hyperbolic/Qwen/Qwen2.5-72B-Instruct", 
    messages=messages
)

print(response)

Streaming

from litellm import completion

os.environ["HYPERBOLIC_API_KEY"] = ""  # your Hyperbolic API key

messages = [{"content": "Write a short poem about AI", "role": "user"}]

# Hyperbolic call with streaming
response = completion(
    model="hyperbolic/deepseek-ai/DeepSeek-V3", 
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

Function Calling

from litellm import completion

os.environ["HYPERBOLIC_API_KEY"] = ""  # your Hyperbolic API key

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get the current weather in a location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA"
                    },
                    "unit": {
                        "type": "string",
                        "enum": ["celsius", "fahrenheit"]
                    }
                },
                "required": ["location"]
            }
        }
    }
]

response = completion(
    model="hyperbolic/deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "What's the weather like in New York?"}],
    tools=tools,
    tool_choice="auto"
)

print(response)

Usage - LiteLLM Proxy

Add the following to your LiteLLM Proxy configuration file:

model_list:
  - model_name: deepseek-fast
    litellm_params:
      model: hyperbolic/deepseek-ai/DeepSeek-V3
      api_key: os.environ/HYPERBOLIC_API_KEY

  - model_name: qwen-coder
    litellm_params:
      model: hyperbolic/Qwen/Qwen2.5-Coder-32B-Instruct
      api_key: os.environ/HYPERBOLIC_API_KEY

  - model_name: deepseek-reasoning
    litellm_params:
      model: hyperbolic/deepseek-ai/DeepSeek-R1
      api_key: os.environ/HYPERBOLIC_API_KEY

Start your LiteLLM Proxy server:

litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Non-streaming response
response = client.chat.completions.create(
    model="deepseek-fast",
    messages=[{"role": "user", "content": "Explain quantum computing in simple terms"}]
)

print(response.choices[0].message.content)

from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Streaming response
response = client.chat.completions.create(
    model="qwen-coder",
    messages=[{"role": "user", "content": "Write a Python function to sort a list"}],
    stream=True
)

for chunk in response:
    if chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

# Configure LiteLLM to use your proxy
response = litellm.completion(
    model="litellm_proxy/deepseek-fast",
    messages=[{"role": "user", "content": "What are the benefits of renewable energy?"}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key"
)

print(response.choices[0].message.content)

# Configure LiteLLM to use your proxy with streaming
response = litellm.completion(
    model="litellm_proxy/qwen-coder",
    messages=[{"role": "user", "content": "Implement a binary search algorithm"}],
    api_base="http://localhost:4000",
    api_key="your-proxy-api-key",
    stream=True
)

for chunk in response:
    if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None:
        print(chunk.choices[0].delta.content, end="")

curl http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer your-proxy-api-key" \\
  -d '{
    "model": "deepseek-fast",
    "messages": [{"role": "user", "content": "What is machine learning?"}]
  }'

curl http://localhost:4000/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -H "Authorization: Bearer your-proxy-api-key" \\
  -d '{
    "model": "qwen-coder",
    "messages": [{"role": "user", "content": "Write a REST API in Python"}],
    "stream": true
  }'

For more detailed information on using the LiteLLM Proxy, see the LiteLLM Proxy documentation.

Supported OpenAI Parameters

Hyperbolic supports the following OpenAI-compatible parameters:

Advanced Usage

Custom API Base

If you're using a custom Hyperbolic deployment:

response = litellm.completion(
    model="hyperbolic/deepseek-ai/DeepSeek-V3",
    messages=[{"role": "user", "content": "Hello"}],
    api_base="https://your-custom-hyperbolic-endpoint.com/v1",
    api_key="your-api-key"
)

Rate Limits

Hyperbolic offers different tiers:

• Basic: 60 requests per minute (RPM)
• Pro: 600 RPM
• Enterprise: Custom limits

Pricing

Hyperbolic offers competitive pay-as-you-go pricing with no hidden fees or long-term commitments. See the model table above for specific pricing per million tokens.

Precision Options

• BF16: Best precision and performance, suitable for tasks where accuracy is critical
• FP8: Optimized for efficiency and speed, ideal for high-throughput applications at lower cost

Additional Resources

• Hyperbolic Official Documentation
• Hyperbolic Dashboard
• API Reference