Atomic Chat

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Installation

• macOS (Apple Silicon): DMG installer
• Windows (x64): EXE installer
• Linux (x86_64): AppImage
• iPhone and iPad: App Store
• Android: Google Play

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Downloading Models

1. Open Atomic Chat and open the model library via the Models tab
2. Search for “LiquidAI”
3. Select a model and quantization level (Q4_K_M recommended)
4. Click Download

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Using the Chat Interface

1. Go to the New Chat tab
2. Select your model from the dropdown
3. Adjust parameters (temperature, top_k, repeat_penalty) in the model settings
4. Start chatting

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Generation Parameters

• temperature (float, default 1.0): Controls randomness (0.0 = deterministic, higher = more random). Typical range: 0.1-2.0
• top_p (float, default 1.0): Nucleus sampling - limits to tokens with cumulative probability ≤ top_p. Typical range: 0.1-1.0
• top_k (int, default 40): Limits to top-k most probable tokens. Typical range: 1-100
• repeat_penalty (float, default 1.0): Penalty for repeating tokens (>1.0 = discourage repetition). Typical range: 1.0-1.5

response = client.chat.completions.create(
    model="<model-id-from-v1-models>",
    messages=[{"role": "user", "content": "What is machine learning?"}],
    temperature=0.1,
    max_tokens=512,
    extra_body={"top_k": 50, "repeat_penalty": 1.05},
)

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Running the Server

1. Load the model in a chat
2. Open the Integrations tab
3. Click Start Server. The server defaults to http://localhost:1337/; use the port shown in the app if you changed it or if that port was unavailable.

curl http://localhost:1337/v1/models

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1337/v1",
    api_key="not-needed"
)

response = client.chat.completions.create(
    model="<model-id-from-v1-models>",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ],
    temperature=0.1,
    max_tokens=512,
    extra_body={"top_k": 50, "repeat_penalty": 1.05},
)
print(response.choices[0].message.content)

​

Streaming Responses

stream = client.chat.completions.create(
    model="<model-id-from-v1-models>",
    messages=[
        {"role": "user", "content": "Tell me a story."}
    ],
    stream=True
)

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

curl http://localhost:1337/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<model-id-from-v1-models>",
    "messages": [{"role": "user", "content": "Hello!"}],
    "temperature": 0.1,
    "top_k": 50,
    "repeat_penalty": 1.05
  }'

​

Vision Models

from openai import OpenAI
import base64

client = OpenAI(
    base_url="http://localhost:1337/v1",
    api_key="not-needed"
)

# Encode image to base64
with open("image.jpg", "rb") as image_file:
    image_data = base64.b64encode(image_file.read()).decode("utf-8")

response = client.chat.completions.create(
    model="<model-id-from-v1-models>",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}},
                {"type": "text", "text": "What's in this image?"}
            ]
        }
    ]
)
print(response.choices[0].message.content)

​

Tips

• Quantization: Q4_K_M offers the best balance of size and quality; step up to Q6_K or Q8_0 if you have memory to spare
• Apple Silicon: GGUF models run with Metal acceleration, and MLX builds of LFM models are supported natively
• Long conversations (desktop): TurboQuant can compress the KV cache to 3 or 4 bits, so long contexts fit in significantly less memory