Cookbook: add Ling-3.0-tiny (#34283)
This commit is contained in:
Xinyuan Tong
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---
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title: Ling-3.0-tiny
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description: "Deploy Ling-3.0-tiny with SGLang — a compact ~7.9B total / ~1.2B active hybrid KDA + MLA MoE in BF16 or FP8, with thinking mode and tool calling."
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tag: NEW
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---
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## Deployment
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<a id="install" />
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<Accordion title="Install SGLang">
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```bash Command
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docker pull lmsysorg/sglang:dev-Ling-3.0-tiny
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```
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For how to launch the image, see [Install → Method 3: Using Docker](../../../docs/get-started/install#method-3-using-docker). Substitute the inner `sglang serve ...` with what the command generator below produces.
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</Accordion>
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Pick your hardware + recipe to generate the launch command. One serving strategy is covered:
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- **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.
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import { Deployment } from "/src/snippets/_deployment.jsx";
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import { config } from "/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx";
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import { benchmarks } from "/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx";
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<Deployment config={config} benchmarks={benchmarks} />
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## Playground
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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.
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import { Playground } from "/src/snippets/_playground.jsx";
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<Playground config={config} />
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## 1. Model Introduction
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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](/cookbook/autoregressive/InclusionAI/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.
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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.
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**Available Models:**
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- **BF16**: [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny) — ~7.9B total / ~1.2B active
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- **FP8** (blockwise E4M3): [inclusionAI/Ling-3.0-tiny-fp8](https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8)
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**License:** MIT
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**Resources:** [HuggingFace](https://huggingface.co/inclusionAI/Ling-3.0-tiny).
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## 2. Configuration Tips
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- 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.
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- Use the dedicated `lmsysorg/sglang:dev-Ling-3.0-tiny` runtime image below; it carries the `bailing_hybrid` support Ling-3.0-tiny needs.
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- 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.
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- 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](#playground).
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- 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.
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- 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.
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- Ling-3.0-tiny ships no built-in MTP draft layer (`num_nextn_predict_layers: 0`), so `--speculative-algorithm NEXTN` is not applicable.
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## 3. Advanced Usage
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### 3.1 Reasoning
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With `--reasoning-parser deepseek-r1` (toggle **Reasoning Parser** in the **Parsers** card of the [Playground above](#playground)), the chain-of-thought is returned in `message.reasoning_content` and the final answer in `message.content`:
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<Accordion title="Thinking-mode request">
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```bash Command
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curl -s http://localhost:30000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "inclusionAI/Ling-3.0-tiny",
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"messages": [{"role": "user", "content": "What is 15% of 240?"}]
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}'
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```
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</Accordion>
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<Accordion title="Example Output">
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```json Output
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{
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": "15% of 240 is **36**.\n\n**Calculation:** 0.15 × 240 = 36",
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"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.",
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"tool_calls": null
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},
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"finish_reason": "stop"
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}
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]
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}
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```
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</Accordion>
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<Note>
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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}`.
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</Note>
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### 3.2 Tool Calling
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With `--tool-call-parser glm45` (toggle **Tool Call Parser** in the **Parsers** card of the [Playground above](#playground)), structured calls are parsed into `message.tool_calls` and `finish_reason` is `tool_calls`:
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<Accordion title="Tool-calling request">
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```bash Command
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curl -s http://localhost:30000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "inclusionAI/Ling-3.0-tiny",
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"messages": [{"role": "user", "content": "Search for the latest news about AI"}],
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"tools": [{
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"type": "function",
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"function": {
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"name": "search",
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"description": "Search for information on the internet",
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"parameters": {
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"type": "object",
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"properties": {
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"query": {"type": "string", "description": "The search query"}
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},
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"required": ["query"]
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}
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}
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}],
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"tool_choice": "auto"
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}'
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```
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</Accordion>
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<Accordion title="Example Output">
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```json Output
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{
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"choices": [
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{
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"message": {
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"role": "assistant",
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"content": "Let me search for the latest news about AI for you.",
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"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.",
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"tool_calls": [
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{
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"id": "call_79b73a89696d4544ac6dd724",
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"index": 0,
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"type": "function",
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"function": { "name": "search", "arguments": "{\"query\": \"latest AI news 2025\"}" }
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}
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]
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},
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"finish_reason": "tool_calls"
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}
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]
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}
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```
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</Accordion>
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For more API examples, see the [SGLang Basic Usage Guide](/docs/basic_usage/send_request).
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