Hyperbolic

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屬性	詳細資訊
說明	Hyperbolic 讓您以傳統雲端成本的一小部分，存取最新模型，並提供相容 OpenAI 的 LLM、圖像生成等 API。
LiteLLM 提供者路由	`hyperbolic/`
提供者文件連結	Hyperbolic 文件 ↗
Base URL	`https://api.hyperbolic.xyz/v1`
支援的操作	`/chat/completions`

https://docs.hyperbolic.xyz

我們支援所有 Hyperbolic 模型；在送出 completion 請求時，只要將 hyperbolic/ 設為前綴即可

可用模型

語言模型

模型	說明	上下文視窗	Pricing per 1M tokens
`hyperbolic/deepseek-ai/DeepSeek-V3`	DeepSeek V3 - 快速且高效率	131,072 tokens	$0.25
`hyperbolic/deepseek-ai/DeepSeek-V3-0324`	DeepSeek V3 2024 年 3 月版本	131,072 tokens	$0.25
`hyperbolic/deepseek-ai/DeepSeek-R1`	DeepSeek R1 - 推理模型	131,072 tokens	$2.00
`hyperbolic/deepseek-ai/DeepSeek-R1-0528`	DeepSeek R1 2028 年 5 月版本	131,072 tokens	$0.25
`hyperbolic/Qwen/Qwen2.5-72B-Instruct`	Qwen 2.5 72B Instruct	131,072 tokens	$0.40
`hyperbolic/Qwen/Qwen2.5-Coder-32B-Instruct`	用於程式碼生成的 Qwen 2.5 Coder 32B	131,072 tokens	$0.20
`hyperbolic/Qwen/Qwen3-235B-A22B`	Qwen 3 235B A22B 變體	131,072 tokens	$2.00
`hyperbolic/Qwen/QwQ-32B`	Qwen QwQ 32B	131,072 tokens	$0.20
`hyperbolic/meta-llama/Llama-3.3-70B-Instruct`	Llama 3.3 70B Instruct	131,072 tokens	$0.80
`hyperbolic/meta-llama/Meta-Llama-3.1-405B-Instruct`	Llama 3.1 405B Instruct	131,072 tokens	$5.00
`hyperbolic/moonshotai/Kimi-K2-Instruct`	Kimi K2 Instruct	131,072 tokens	$2.00

必要變數

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

從 Hyperbolic 儀表板取得您的 API 金鑰。

用法 - LiteLLM Python SDK

非串流

Hyperbolic Non-streaming Completion
import os
import litellm
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)

串流

Hyperbolic Streaming Completion
import os
import litellm
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)

函式呼叫

Hyperbolic Function Calling
import os
import litellm
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)

用法 - LiteLLM Proxy

將以下內容加入您的 LiteLLM Proxy 設定檔：

config.yaml
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

啟動您的 LiteLLM Proxy 伺服器：

Start LiteLLM Proxy
litellm --config config.yaml

# RUNNING on http://0.0.0.0:4000

OpenAI SDK
LiteLLM SDK
cURL

Hyperbolic via Proxy - Non-streaming
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)

Hyperbolic via Proxy - Streaming
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="")

Hyperbolic via Proxy - LiteLLM SDK
import litellm

# 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)

Hyperbolic via Proxy - LiteLLM SDK Streaming
import litellm

# 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="")

Hyperbolic via Proxy - cURL
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?"}]
  }'

Hyperbolic via Proxy - cURL Streaming
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
  }'

如需更詳細的 LiteLLM Proxy 使用資訊，請參閱 LiteLLM Proxy 文件。

支援的 OpenAI 參數

Hyperbolic 支援以下與 OpenAI 相容的參數：

參數	類型	說明
`messages`	array	必要。包含 'role' 和 'content' 的訊息物件陣列
`model`	string	必要。模型 ID（例如 deepseek-ai/DeepSeek-V3、Qwen/Qwen2.5-72B-Instruct）
`stream`	boolean	選用。啟用串流回應
`temperature`	float	選用。採樣溫度（0.0 到 2.0）
`top_p`	float	選用。核採樣參數
`max_tokens`	integer	選用。要生成的最大 token 數
`frequency_penalty`	float	選用。對高頻 token 進行懲罰
`presence_penalty`	float	選用。根據存在與否對 token 進行懲罰
`stop`	string/array	選用。停止序列
`n`	integer	選用。要生成的 completion 數量
`tools`	array	選用。可用工具/函式清單
`tool_choice`	string/object	選用。控制工具/函式呼叫
`response_format`	object	選用。回應格式規格
`seed`	integer	選用。用於重現性的隨機種子
`user`	string	選用。使用者識別碼

進階用法

自訂 API Base

如果您使用的是自訂 Hyperbolic 部署：

Custom API Base
import litellm

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"
)

速率限制

Hyperbolic 提供不同方案：

Basic：每分鐘 60 次請求（RPM）
Pro：600 RPM
Enterprise：自訂限制

定價

Hyperbolic 提供具競爭力的按用量付費定價，沒有隱藏費用或長期承諾。每百萬 token 的具體價格請參閱上方模型表。

精度選項

BF16：最佳精度與效能，適合對準確性要求嚴格的任務
FP8：針對效率與速度最佳化，適合在較低成本下追求高吞吐量的應用

總覽​

可用模型​

語言模型​

必要變數​

用法 - LiteLLM Python SDK​

非串流​

串流​

函式呼叫​

用法 - LiteLLM Proxy​

支援的 OpenAI 參數​

進階用法​

自訂 API Base​

速率限制​

定價​

精度選項​

其他資源​

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