Ling-3.0-tiny - SGLang Documentation

Deployment

Install SGLang

Command

docker pull lmsysorg/sglang:dev-Ling-3.0-tiny

For how to launch the image, see Install → Method 3: Using Docker. Substitute the inner sglang serve ... with what the command generator below produces.

Pick your hardware + recipe to generate the launch command. One serving strategy is covered:

High-Throughput — most tokens per second across many users. Best for batch jobs. Ling-3.0-tiny ships no built-in MTP draft layer (num_nextn_predict_layers: 0), so there is no NEXTN speculative-decoding recipe.

Playground

The Playground is where you experiment with SGLang features beyond the documented matrix. The Deploy panel above only emits the curated recipe combinations on this page; the Playground lets you turn on additional knobs on top of whichever cell the Deploy panel is currently showing.

1. Model Introduction

Ling-3.0-tiny is a compact hybrid-attention Mixture-of-Experts (MoE) language model from the BailingMoeV3 family — the small variant of Ling-3.0-flash. It interleaves Kimi Delta Attention (KDA) linear-attention layers with gated Multi-head Latent Attention (MLA) full-attention layers on top of a fine-grained MoE feed-forward network, keeping per-token inference cost near a ~1B dense model — ~7.9B total parameters with ~1.2B active — while retaining large-model capacity. It is a thinking model with chain-of-thought enabled by default, and it supports structured tool calling. Native context length is 128K. Unlike Ling-3.0-flash, it ships no built-in MTP draft layer, so it does not use NEXTN speculative decoding. Available Models:

BF16: inclusionAI/Ling-3.0-tiny — ~7.9B total / ~1.2B active
FP8 (blockwise E4M3): inclusionAI/Ling-3.0-tiny-fp8

License: MIT Resources: HuggingFace.

2. Configuration Tips

At ~7.9B total / 15.8 GB in BF16 (~7.9 GB in FP8), a single GPU is plenty on every supported card. Tensor parallelism is only useful to raise aggregate KV-cache capacity for many long-context concurrent requests — add --tp 2/--tp 4 to a multi-GPU serve directly.
Use the dedicated lmsysorg/sglang:dev-Ling-3.0-tiny runtime image below; it carries the bailing_hybrid support Ling-3.0-tiny needs.
The FP8 checkpoint uses blockwise (128×128) E4M3 weights with dynamic activations, quantized from the BF16 model with attention projections, the dense MoE gate, and the lm_head left in higher precision. SGLang detects the format from the checkpoint’s quantization_config, so no explicit quantization flag is needed, and the same single-GPU recipe serves it.
Unlike Ling-3.0-flash (which pairs --reasoning-parser ling3 / --tool-call-parser ling3), Ling-3.0-tiny uses --reasoning-parser deepseek-r1 and --tool-call-parser glm45 (its auto-detected template pairing) — the template wraps tool calls in <tool_call> blocks and emits an inline ...</think> chain-of-thought. Toggle them in the Parsers card of the Playground.
Only --model-path, --host, and --port are needed. SGLang auto-resolves the context length (native 128K from max_position_embeddings), the attention backend, and --mem-fraction-static from the GPU and the CUDA-graph runtime, so the recipes leave them unset.
The chat template defaults to thinking on. Turn it off per request with "chat_template_kwargs": {"enable_thinking": false} for direct answers without the ...</think> block.
Ling-3.0-tiny ships no built-in MTP draft layer (num_nextn_predict_layers: 0), so --speculative-algorithm NEXTN is not applicable.

3. Advanced Usage

3.1 Reasoning

With --reasoning-parser deepseek-r1 (toggle Reasoning Parser in the Parsers card of the Playground above), the chain-of-thought is returned in message.reasoning_content and the final answer in message.content:

Thinking-mode request

Command

curl -s http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inclusionAI/Ling-3.0-tiny",
    "messages": [{"role": "user", "content": "What is 15% of 240?"}]
  }'

Example Output

Output

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "15% of 240 is **36**.\n\n**Calculation:** 0.15 × 240 = 36",
        "reasoning_content": "The user is asking for 15% of 240. This is a simple percentage calculation.\n\n15% of 240 = 0.15 × 240 = 36\n\nLet me verify: 0.15 × 240 = 0.15 × 200 + 0.15 × 40 = 30 + 6 = 36. Yes, that's correct.",
        "tool_calls": null
      },
      "finish_reason": "stop"
    }
  ]
}

Thinking is controlled by the chat template’s enable_thinking kwarg and is on by default. Disable it per request with "chat_template_kwargs": {"enable_thinking": false}.

3.2 Tool Calling

With --tool-call-parser glm45 (toggle Tool Call Parser in the Parsers card of the Playground above), structured calls are parsed into message.tool_calls and finish_reason is tool_calls:

Tool-calling request

Command

curl -s http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "inclusionAI/Ling-3.0-tiny",
    "messages": [{"role": "user", "content": "Search for the latest news about AI"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "search",
        "description": "Search for information on the internet",
        "parameters": {
          "type": "object",
          "properties": {
            "query": {"type": "string", "description": "The search query"}
          },
          "required": ["query"]
        }
      }
    }],
    "tool_choice": "auto"
  }'

Example Output

Output

{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Let me search for the latest news about AI for you.",
        "reasoning_content": "The user wants me to search for the latest news about AI. I'll use the search tool to find recent AI news.",
        "tool_calls": [
          {
            "id": "call_79b73a89696d4544ac6dd724",
            "index": 0,
            "type": "function",
            "function": { "name": "search", "arguments": "{\"query\": \"latest AI news 2025\"}" }
          }
        ]
      },
      "finish_reason": "tool_calls"
    }
  ]
}

For more API examples, see the SGLang Basic Usage Guide.

​Deployment

​Playground

​1. Model Introduction

​2. Configuration Tips

​3. Advanced Usage

​3.1 Reasoning

​3.2 Tool Calling

Deployment

Playground

1. Model Introduction

2. Configuration Tips

3. Advanced Usage

3.1 Reasoning

3.2 Tool Calling