inclusionAI/Ling-3.0-flash · Hugging Face

Source: https://huggingface.co/inclusionAI/Ling-3.0-flash

🤗Hugging Face| 🤖ModelScope| 🐙OpenRouter

https://huggingface.co/inclusionAI/Ling-3.0-flash#introductionIntroduction

We’re introducing Ling-3.0-flash, our next-generation native hybrid reasoning model. Operating with124Btotal and5.1Bactive parameters (~12.4% and ~8.1% of our previous 1T-class flagship Ring-2.6-1T), Ling-3.0-flash matches or outperforms its predecessor across key benchmarks.

Key highlights of the model are summarized below:

• **Native Hybrid-Linear Architecture:**Ling-3.0 adopts a native hybrid linear attention architecture from the very start of pretraining (5:1 alternating stacking of Kimi Delta Attention (KDA) and MLA), upgraded with KDA fine-grained diagonal gating and 1/64 sparse MoE. With 124B total parameters and 5.1B activated parameters, it achieves a synergistic leap in long-context efficiency and computational cost.
• **Remarkable Efficiency & Performance:**Engineered for speed, compute efficiency, and production deployment, Ling-3.0-flash delivers class-defying performance against both larger SOTA competitors and previous-generation flagships. Activating only 5.1B parameters per token, it provides impressive reasoning, instruction following, and long-context capabilities to empower complex agentic workflows in production environments.
• **Comprehensive Agentic Evolution:**Tailored for real-world productivity workflows, the model incorporates 10,000+ interactive training environments to achieve end-to-end closed-loop execution across Coding, General, and Deep Research Agent tasks. It natively integrates the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios.s the SGLang HiCache + Mooncake hierarchical caching architecture (featuring physical dual-pools and a cluster-shared L3 cache), eliminating redundant recomputation during long-horizon interactions and reducing Time to First Token (TTFT) by 60% to over 80% in long-input scenarios.

https://huggingface.co/inclusionAI/Ling-3.0-flash#model-overviewModel Overview

The model summary information and architecture diagram are as follows:

ArchitectureHybrid-linear MoEParameter ScaleTotoal 124B, Activated 5.1BTransformer Layers35 KDA + 7 Gated MLA (5:1)Number of Dense Layers2Number of Routed Experts512Number of Shared Experts1Number of Activated Experts8Attention Heads32Hidden Size2560Expert Intermediate Size768Dense Intermediate Size6144Vocabulary Size157184Context Training Schedule8K -> 32K -> 256K

https://huggingface.co/inclusionAI/Ling-3.0-flash#evaluationEvaluation

We have conducted a comprehensive evaluation of Ling-3.0-flash across multiple authoritative benchmarks.Ling-3.0-flashperforms strongly on representative code/agent benchmarks such asSWE-Bench Pro, SWE-Bench Multilingual, Tau3-banking-AA,MCP-AtlasandSkillsBench, etc. In practice, Ling-3.0-flash delivers a strong user experience across frameworks includingClaude Code,Kilo Code,Qwen Code,Hermes Agent,andOpenClaw, etc. Beyond agentic tasks, Ling-3.0-flash also delivers strong performance acrossgeneral knowledge,mathematical reasoning,instruction following,andlong-context understanding.

• Thinking mode is enabled by default. Unless otherwise specified, the default parameters for Ling-3.0-flash are as follows:temperature=0\.6, top\_p=0\.95, top\_k=20. - SWE-Bench Series:Evaluated using OpenHands as the agent harness with tailored prompts. Decoding usestemperature=0\.6, top\_p=0\.95, max\_new\_tokens=32K, with a 256K context window. - Terminal-Bench 2.1: Evaluated under the Artificial Analysis (AA) protocol using the default Terminus 2 harness, a unified 2-hour timeout, the provided JSON parser in preserve-thinking mode, and 3 runs per task (mean). Decoding usestemperature=0\.6, top\_p=1\.0, max\_new\_tokens=32K, with a 256K context window. - MiniAppBench: A 500-task coding benchmark evaluating whether models can turn a single user request into complete, usable interactive HTML apps in real-world application-generation scenarios. Evaluated withtemperature=1\.0, top\_p=1\.0, max\_tokens=128K.

https://huggingface.co/inclusionAI/Ling-3.0-flash#quickstartQuickstart

https://huggingface.co/inclusionAI/Ling-3.0-flash#sglangSGlang

https://huggingface.co/inclusionAI/Ling-3.0-flash#install-our-sglangInstall our SGLang

pip install uv

uv venv ~/my_ling_env

source ~/my_ling_env/bin/activate

git clone -b ling_v3_support https://github.com/inclusionAI/sglang_ling_v3.git

cd sglang_ling_v3

pip install --upgrade pip

pip install -e "python"

https://huggingface.co/inclusionAI/Ling-3.0-flash#run-inferenceRun Inference

Here is an example to run Ling-3.0-flash with 4 GPUs, where the master node IP is$\{MASTER\_IP\}and server port is$\{PORT\}:

Server

Since the model is trained with MTP, we recommend enabling MTP during inference (i.e., --speculative-algorithm NEXTN) for lower latency.

export SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1
export SGLANG_JIT_DEEPGEMM_PRECOMPILE=1
export SGLANG_ENABLE_SPEC_V2=1
python -m sglang.launch_server \
    --model-path $MODEL_PATH \
    --dist-init-addr $MASTER_IP:2345 \
    --port $PORT \
    --nnodes 1 \
    --mem-fraction-static 0.8 \
    --max-running-requests 64 \
    --tp-size 4 \
    --chunked-prefill-size 8192 \
    --tool-call-parser ling3 \
    --reasoning-parser ling3 \
    --context-length 262144 \
    --speculative-algorithm NEXTN \
    --max-mamba-cache-size 320 \
    --enable-fp32-lm-head \
    --disable-shared-experts-fusion

Client

We recommend using the sampling parameterstemperature=0\.6,top\_p=0\.95, andtop\_k=20, and enablingenable\_thinkingfor better performance.

curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto",
       "messages": [{"role": "user", "content": "hello!"}],
       "chat_template_kwargs": {"enable_thinking": true},
       "stream": true,
       "temperature": 0.6, 
       "top_k": 20,
       "top_p": 0.95
     }'

https://huggingface.co/inclusionAI/Ling-3.0-flash#vllmvLLM

https://huggingface.co/inclusionAI/Ling-3.0-flash#install-our-vllmInstall our vLLM

pip install uv

uv venv ~/my_ling_env

source ~/my_ling_env/bin/activate

git clone -b ling_3_0 https://github.com/inclusionAI/vllm-ling-v3.git

cd vllm-ling-v3

VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto

https://huggingface.co/inclusionAI/Ling-3.0-flash#run-inference-1Run Inference

Here is the example to run Ling-3.0-flash with 4 GPUs, where the server port is$\{PORT\}:

Server

Since the model is trained with MTP, we recommend enabling MTP during inference (i.e., --speculative-config) for lower latency.

vllm serve "$MODEL_PATH" \
    --port "$PORT" \
    --trust-remote-code \
    --served-model-name auto \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.85 \
    --enable-prefix-caching \
    --mamba-cache-mode align \
    --enable-auto-tool-choice \
    --tool-call-parser ling3 \
    --reasoning-parser ling3 \
    --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

Client

We recommend using the sampling parameterstemperature=0\.6,top\_p=0\.95, andtop\_k=20, and enablingenable\_thinkingfor better performance.

curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "auto",
       "messages": [{"role": "user", "content": "hello!"}],
       "chat_template_kwargs": {"enable_thinking": true},
       "stream": true,
       "temperature": 0.6, 
       "top_k": 20,
       "top_p": 0.95
     }'

Spaces usinginclusionAI/Ling-3.0-flash12

Evaluation resultshttps://huggingface.co/docs/hub/eval-results