From 77c90e7e54938f9835c8d27563183f1ca2def4a6 Mon Sep 17 00:00:00 2001 From: Xinyuan Tong <115166877+JustinTong0323@users.noreply.github.com> Date: Mon, 10 Aug 2026 21:25:17 +0800 Subject: [PATCH] Cookbook: add Ling-3.0-tiny (#34283) --- .../InclusionAI/Ling-3.0-tiny.mdx | 166 +++++++++++++++ docs/docs.json | 1 + .../inclusionAI/ling-3.0-tiny-benchmarks.jsx | 28 +++ .../configs/inclusionAI/ling-3.0-tiny.jsx | 191 ++++++++++++++++++ 4 files changed, 386 insertions(+) create mode 100644 docs/cookbook/autoregressive/InclusionAI/Ling-3.0-tiny.mdx create mode 100644 docs/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx create mode 100644 docs/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx diff --git a/docs/cookbook/autoregressive/InclusionAI/Ling-3.0-tiny.mdx b/docs/cookbook/autoregressive/InclusionAI/Ling-3.0-tiny.mdx new file mode 100644 index 000000000..5c1a9f6f2 --- /dev/null +++ b/docs/cookbook/autoregressive/InclusionAI/Ling-3.0-tiny.mdx @@ -0,0 +1,166 @@ +--- +title: Ling-3.0-tiny +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." +tag: NEW +--- + +## Deployment + + + + + +```bash Command +docker pull lmsysorg/sglang:dev-Ling-3.0-tiny +``` + +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. + + + +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. + +import { Deployment } from "/src/snippets/_deployment.jsx"; +import { config } from "/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx"; +import { benchmarks } from "/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx"; + + + +## 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. + +import { Playground } from "/src/snippets/_playground.jsx"; + + + +## 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](/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. + +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](https://huggingface.co/inclusionAI/Ling-3.0-tiny) — ~7.9B total / ~1.2B active +- **FP8** (blockwise E4M3): [inclusionAI/Ling-3.0-tiny-fp8](https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8) + +**License:** MIT + +**Resources:** [HuggingFace](https://huggingface.co/inclusionAI/Ling-3.0-tiny). + +## 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 `` blocks and emits an inline `...` chain-of-thought. Toggle them in the **Parsers** card of the [Playground](#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 `...` 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](#playground)), the chain-of-thought is returned in `message.reasoning_content` and the final answer in `message.content`: + + + +```bash 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?"}] + }' +``` + + + + + +```json 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](#playground)), structured calls are parsed into `message.tool_calls` and `finish_reason` is `tool_calls`: + + + +```bash 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" + }' +``` + + + + + +```json 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](/docs/basic_usage/send_request). diff --git a/docs/docs.json b/docs/docs.json index b7eb1e8c9..f07376dd2 100644 --- a/docs/docs.json +++ b/docs/docs.json @@ -1340,6 +1340,7 @@ "group": "InclusionAI", "pages": [ "cookbook/autoregressive/InclusionAI/Ling-3.0-flash", + "cookbook/autoregressive/InclusionAI/Ling-3.0-tiny", "cookbook/autoregressive/InclusionAI/Ring-2.6-1T", "cookbook/autoregressive/InclusionAI/Ling-2.6", "cookbook/autoregressive/InclusionAI/Ling-2.5-1T", diff --git a/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx b/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx new file mode 100644 index 000000000..86bab1a42 --- /dev/null +++ b/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny-benchmarks.jsx @@ -0,0 +1,28 @@ +// Measured on lmsysorg/sglang:dev-Ling-3.0-tiny, 1× H200. TTFT/TPOT are P50 +// (median) from sglang.bench_serving (random ISL 8192 / OSL 1024, --flush-cache); +// tokens_per_sec_per_gpu = output tok/s × (isl+osl)/osl. Accuracy from sgl-eval +// full GSM8K (1319). +export const benchmarks = [ + { + match: { hw: "h200", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + sglang_version: "dev-Ling-3.0-tiny", + speed: [ + { workload: { dataset: "random", isl: 8192, osl: 1024, max_concurrency: 1 }, + ttft_ms: 69.95, tpot_ms: 2.87, tokens_per_sec_per_gpu: 3002 }, + { workload: { dataset: "random", isl: 8192, osl: 1024, max_concurrency: 16 }, + ttft_ms: 78.31, tpot_ms: 5.96, tokens_per_sec_per_gpu: 22446 }, + ], + accuracy: { gsm8k_pct: 94.01 }, + }, + { + match: { hw: "h200", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + sglang_version: "dev-Ling-3.0-tiny", + speed: [ + { workload: { dataset: "random", isl: 8192, osl: 1024, max_concurrency: 1 }, + ttft_ms: 81.78, tpot_ms: 2.82, tokens_per_sec_per_gpu: 3072 }, + { workload: { dataset: "random", isl: 8192, osl: 1024, max_concurrency: 16 }, + ttft_ms: 79.50, tpot_ms: 5.57, tokens_per_sec_per_gpu: 23738 }, + ], + accuracy: { gsm8k_pct: 94.69 }, + }, +]; diff --git a/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx b/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx new file mode 100644 index 000000000..36f7de2c1 --- /dev/null +++ b/docs/src/snippets/configs/inclusionAI/ling-3.0-tiny.jsx @@ -0,0 +1,191 @@ +export const config = { + modelName: "Ling-3.0-tiny", + + supportedHardware: ["h20-3e", "h200", "h800", "h100", "b200", "gb300"], + groupHardware: false, + + variants: [ + { id: "default", label: "Ling-3.0-tiny" }, + ], + quantizations: [ + { id: "bf16", label: "BF16" }, + { id: "fp8", label: "FP8" }, + ], + strategies: [ + { id: "high-throughput", label: "High-Throughput" }, + ], + nodesOptions: [ + { id: "single", label: "Single Node" }, + ], + + modelNames: { + "default|bf16": "inclusionAI/Ling-3.0-tiny", + "default|fp8": "inclusionAI/Ling-3.0-tiny-fp8", + }, + + placeholders: { + HOST_IP: { target: "command", label: "Bind host", default: "0.0.0.0" }, + PORT: { target: "command", label: "Bind port", default: "30000" }, + HF_TOKEN: { target: "command", label: "HF token (Docker)", default: "" }, + CURL_HOST: { target: "curl", label: "Server host", default: "localhost" }, + CURL_PORT: { target: "curl", label: "Server port", default: "30000" }, + }, + + curl: `curl http://{{CURL_HOST}}:{{CURL_PORT}}/v1/chat/completions \\ +-H 'Content-Type: application/json' \\ +-d '{ "model": "{{MODEL_NAME}}", "messages": [{"role":"user","content":"What is the capital of France?"}] }'`, + + dockerImages: { + "h20-3e": "lmsysorg/sglang:dev-Ling-3.0-tiny", + "h200": "lmsysorg/sglang:dev-Ling-3.0-tiny", + "h800": "lmsysorg/sglang:dev-Ling-3.0-tiny", + "h100": "lmsysorg/sglang:dev-Ling-3.0-tiny", + "b200": "lmsysorg/sglang:dev-Ling-3.0-tiny", + "gb300": "lmsysorg/sglang:dev-Ling-3.0-tiny", + }, + + benchmarkCommands: { + speed: `python3 -m sglang.bench_serving \\ + --backend sglang \\ + --host {{CURL_HOST}} --port {{CURL_PORT}} \\ + --model {{MODEL_NAME}} \\ + --dataset-name {{DATASET}} \\ + --random-input-len {{ISL}} --random-output-len {{OSL}} \\ + --num-prompts {{NUM_PROMPTS}} --max-concurrency {{MAX_CONCURRENCY}} \\ + --flush-cache`, + accuracy: { + gsm8k_pct: `# To install sgl-eval: pip install git+https://github.com/sgl-project/sgl-eval +sgl-eval run gsm8k \\ + --base-url http://{{CURL_HOST}}:{{CURL_PORT}}/v1 \\ + --num-threads 32`, + }, + }, + + accuracyLabels: [ + ["gsm8k_pct", "GSM8K", "%"], + ], + + github: { + cookbookModel: "inclusionAI/Ling-3.0-tiny", + }, + + playgroundFeatures: { + parsers: { + items: [ + { id: "reasoning", label: "Reasoning Parser", flag: "--reasoning-parser deepseek-r1" }, + { id: "toolCall", label: "Tool Call Parser", flag: "--tool-call-parser glm45" }, + ], + }, + }, + + cells: [ + { + match: { hw: "h200", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: true, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h20-3e", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h800", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h100", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "b200", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "gb300", variant: "default", quant: "bf16", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h20-3e", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h200", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: true, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h800", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "h100", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "b200", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + { + match: { hw: "gb300", variant: "default", quant: "fp8", strategy: "high-throughput", nodes: "single" }, + verified: false, + flags: [ + "--model-path {{MODEL_NAME}}", + "--host {{HOST_IP}}", + "--port {{PORT}}", + ], + }, + ], +};