Chroma-1.0 - SGLang Documentation

1. Model Introduction

Chroma-1.0 is an open-source end-to-end speech conversation model developed by FlashLabs, focusing on the following core capabilities:

Real-time Speech Generation: Supports low-latency speech synthesis, suitable for real-time conversational scenarios.
Customized Voice Cloning: Capable of cloning and replicating specific speaker voice characteristics.
End-to-End Architecture: Provides a complete processing workflow from speech to speech.
Speech Reasoning: Equipped with reasoning capabilities to understand and process speech content.

2. Architecture Overview

Chroma-1.0 utilizes a hybrid serving architecture rather than a direct SGLang deployment. This design choice is driven by:

Complex Model Architecture: The end-to-end speech processing pipeline involves specialized components that go beyond standard text generation loops.
KV Cache & State Management: The model requires custom handling of KV caches that differs from standard implementations.
Batching Limitations: The current implementation supports a batch size of 1, meaning SGLang’s advanced continuous batching capabilities are not yet fully applicable.

Therefore, you will start the FlashLabs Server, which manages the overall workflow and selectively leverages SGLang for specific inference components where supported.

Outer Layer: FlashLabs Server (Handles Audio I/O, State, and Model Logic)
Inner Engine: SGLang Instance (Utilized for specific acceleration where applicable)

3. Installation & Setup

We recommend following these steps to set up the environment and prepare the model.

Step 1: Get the Docker Image

Pull the official pre-built image from Docker Hub to ensure all dependencies are correctly configured.

Command

docker pull flashlabs/chroma:latest

Step 2: Download Model Weights

Download the Chroma-4B weights from Hugging Face. You can choose one of the following methods: Method 1: Using Python (Recommended)

Command

huggingface-cli download FlashLabs/Chroma-4B --local-dir Chroma-4B

Method 2: Using Git Clone Make sure you have Git LFS installed before cloning.

Command

# Install Git LFS first
git lfs install

# Clone the repository
git clone https://huggingface.co/FlashLabs/Chroma-4B Chroma-4B

Step 3: Download Chroma Codes (SGLang version)

Command

git clone https://github.com/FlashLabs-AI-Corp/Chroma-SGLang.git

cd Chroma-SGLang

Step 4: Run the Server

Command

docker run -d \
  --gpus all \
  -p 8000:8000 \
  -w /app/Chroma-SGLang \
  -v "your_Chroma-SGLang_path":/app/Chroma-SGLang \
  -v "your_chroma_path":/model \
  -e CHROMA_MODEL_PATH=/model \
  -e DP_SIZE="1" \
  flashlabs/chroma:latest \
  /opt/conda/bin/python -m uvicorn api_server:app \
  --host 0.0.0.0 \
  --port 8000 \
  --workers 1

or run simply the following one line command

Command

docker-compose up -d

5. Client Usage Example

Once the server is running, you can interact with it using HTTP requests.

Python Client

Example

import requests
import base64

url = "http://localhost:8000/v1/chat/completions"
headers = {"Content-Type": "application/json"}

payload = {
    "model": "chroma",
    "messages": [
        {
            "role": "system",
            "content": "You are Chroma, a voice agent developed by FlashLabs."
        },
        {
            "role": "user",
            "content": [
                {"type": "audio", "audio": "assets/question_audio.wav"}
            ]
        }
    ],
    "max_tokens": 1000,
    "return_audio": True
}

response = requests.post(url, json=payload, headers=headers)
result = response.json()

if result.get("audio"):
    audio_data = base64.b64decode(result["audio"])
    with open("output.wav", "wb") as f:
        f.write(audio_data)
    print("Audio saved to output.wav")

OpenAI SDK Compatible Example

Example

from openai import OpenAI

client = OpenAI(
    api_key="dummy",
    base_url="http://localhost:8000/v1"
)

response = client.chat.completions.create(
    model="chroma",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {
            "role": "user",
            "content": [
                {"type": "audio", "audio": "assets/question_audio.wav"}
            ]
        }
    ],
    extra_body={
        "prompt_text": "I have not... I'm so exhausted, I haven't slept in a very long time. It could be because... Well, I used our... Uh, I'm, I just use... This is what I use every day. I use our cleanser every day, I use serum in the morning and then the moistu- daily moisturizer. That's what I use every morning.",
        "prompt_audio": "assets/ref_audio.wav",
        "return_audio": True
    }
)

print(response)

CLI (cURL)

Command

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "chroma",
    "messages": [
      {
        "role": "system",
        "content": "You are Chroma, a voice agent developed by FlashLabs."
      },
      {
        "role": "user",
        "content": [
          {
            "type": "audio",
            "audio": "assets/question_audio.wav"
          }
        ]
      }
    ],
    "max_tokens": 1000,
    "return_audio": true
  }' | jq -r '.audio' | base64 -d > output.wav

​1. Model Introduction

​2. Architecture Overview

​3. Installation & Setup

​Step 1: Get the Docker Image

​Step 2: Download Model Weights

​Step 3: Download Chroma Codes (SGLang version)

​Step 4: Run the Server

​5. Client Usage Example

​Python Client

​OpenAI SDK Compatible Example

​CLI (cURL)

1. Model Introduction

2. Architecture Overview

3. Installation & Setup

Step 1: Get the Docker Image

Step 2: Download Model Weights

Step 3: Download Chroma Codes (SGLang version)

Step 4: Run the Server

5. Client Usage Example

Python Client

OpenAI SDK Compatible Example

CLI (cURL)