swarmauri_llm_hyperbolic 0.11.0.dev1

Provider-specific Swarmauri import package for Hyperbolic OpenAI-compatible chat, vision-language inference, streaming, and text-to-speech workflows.

Swarmauri Hyperbolic LLM

swarmauri_llm_hyperbolic provides provider-specific imports for Hyperbolic inference in Swarmauri applications. The package exports text chat, vision-language, and text-to-speech adapters while keeping implementation parity with swarmauri_standard.

Hyperbolic's inference documentation describes OpenAI-compatible chat completion APIs under https://api.hyperbolic.xyz/v1/chat/completions, model catalog discovery under /v1/models, vision-language models that accept images alongside text, and audio generation through /v1/audio/generation. This package maps those provider surfaces into Swarmauri components:

• HyperbolicModel for OpenAI-compatible text chat completions.
• HyperbolicVisionModel for vision-language chat completions with image input.
• HyperbolicAudioTTS for text-to-speech audio generation.

Why Use This Package?

• Keep Hyperbolic-specific inference imports explicit in Swarmauri applications.
• Use Swarmauri Conversation objects with Hyperbolic's OpenAI-compatible chat and vision-language endpoints.
• Add text-to-speech generation without wiring raw base64 audio responses in application code.
• Preserve a provider package boundary while relying on shared swarmauri_standard runtime implementations.

FAQ

What does swarmauri_llm_hyperbolic install?

It installs provider package entry points for HyperbolicModel, HyperbolicVisionModel, and HyperbolicAudioTTS.

Which Hyperbolic endpoints does it use?

HyperbolicModel and HyperbolicVisionModel use Hyperbolic's OpenAI-compatible chat/completions endpoint. HyperbolicAudioTTS posts to https://api.hyperbolic.xyz/v1/audio/generation and writes the returned base64 audio payload to a local file.

Does it support streaming?

HyperbolicModel supports synchronous and asynchronous streaming for chat completion responses. HyperbolicAudioTTS does not support streaming and raises NotImplementedError for stream and astream.

Does it support vision-language prompts?

Yes. HyperbolicVisionModel accepts Swarmauri message content lists with text and image input. Local image file paths can be converted to base64 data URLs by the runtime helper.

Is the TTS adapter a general LLM?

No. HyperbolicAudioTTS is a text-to-speech adapter kept in this provider package for compatibility. The runtime deprecation warning points new projects toward newer Swarmauri TTS imports when available.

Features

• HyperbolicModel for sync, async, streaming, and batch chat completion workflows.
• HyperbolicVisionModel for image-plus-text prompts against Hyperbolic VLMs.
• HyperbolicAudioTTS for synchronous and asynchronous text-to-speech generation.
• Model discovery through Hyperbolic's /models endpoint.
• Generation controls including temperature, max_tokens, top_p, top_k, and stop.
• Optional usage metadata handling on chat responses when available.
• Compatibility with Python 3.10, 3.11, 3.12, 3.13, and 3.14.

Installation

uv add swarmauri_llm_hyperbolic

pip install swarmauri_llm_hyperbolic

Prerequisites

Create a Hyperbolic API key in the Hyperbolic app and pass it to the model constructor as api_key=....

Usage

from swarmauri_llm_hyperbolic import HyperbolicModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage

conversation = Conversation()
conversation.add_message(HumanMessage(content="Summarize this deployment risk."))

model = HyperbolicModel(api_key="HYPERBOLIC_API_KEY")
result = model.predict(conversation=conversation, max_tokens=200)

print(result.get_last().content)

Streaming Chat

from swarmauri_llm_hyperbolic import HyperbolicModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage

conversation = Conversation()
conversation.add_message(HumanMessage(content="List three API gateway checks."))

model = HyperbolicModel(api_key="HYPERBOLIC_API_KEY")

for token in model.stream(conversation=conversation):
    print(token, end="")

Vision-Language Prompt

from swarmauri_llm_hyperbolic import HyperbolicVisionModel
from swarmauri_standard.conversations.Conversation import Conversation
from swarmauri_standard.messages.HumanMessage import HumanMessage

conversation = Conversation()
conversation.add_message(
    HumanMessage(
        content=[
            {"type": "text", "text": "Describe the image."},
            {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}},
        ]
    )
)

model = HyperbolicVisionModel(api_key="HYPERBOLIC_API_KEY")
result = model.predict(conversation=conversation)

print(result.get_last().content)

Text To Speech

from swarmauri_llm_hyperbolic import HyperbolicAudioTTS

tts = HyperbolicAudioTTS(
    api_key="HYPERBOLIC_API_KEY",
    language="EN",
    speaker="EN-US",
    speed=1.0,
)

audio_path = tts.predict("Swarmauri turns model providers into components.")
print(audio_path)

Related Packages

• swarmauri_llm_deepinfra
• swarmauri_llm_groq
• swarmauri_llm_mistral
• swarmauri_llm_openai
• swarmauri_llm_perplexity
• swarmauri_llm_playht

Foundational Swarmauri Packages

• swarmauri_core
• swarmauri_base
• swarmauri_standard
• swarmauri

Provider Documentation

• Hyperbolic quick start
• Hyperbolic text generation APIs
• Hyperbolic vision-language APIs
• Hyperbolic supported models

Best Practices

• Store Hyperbolic API keys in environment variables or a secrets manager.
• Confirm model availability and provider capability flags before production deployment.
• Use HyperbolicVisionModel only for image-aware prompts; plain chat is simpler with HyperbolicModel.
• Use explicit output paths for TTS generation so generated audio files are easy to manage.
• Prefer newer Swarmauri VLM and TTS imports for new projects when available.

License

Apache-2.0