inclusionAI/Ling-3.1-flash-1019

text-generationUpstream checksumssafetensorsbailing_hybridtext-generationconversationalcustom_codelicense:mit

License: mit
Size: not mirrored yet
Downloads in the last 30 days: 0 / 30 days

No official torrent yet — requesting it tells the flock what to host next. You cannot seed a catalog-only page.

Cataloged from inclusionAI/Ling-3.1-flash-1019

🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter

Introduction

We introduce Ling-3.1-flash-1019, an upgraded version of Ling-3.1-flash, with 560B total parameters, 25B active parameters per token, and a 1M-token context window. With further post-training optimizations, Ling-3.1-flash-1019 demonstrates significant improvements in reasoning and real-world agentic capabilities. Its AA-Briefcase score rises from approximately 1400 to 1524 Elo, while performance on AutomationBench-AA increases from 62% to 68%. The model also makes gains on scientific benchmarks, including CritPt (18% → 26%) and SciCode (54% → 57%). Overall, it scores 44 on the Artificial Analysis Intelligence Index v4.3.2, up from 41 for its predecessor. These gains highlight the model's improved capabilities in complex reasoning, multi-step task execution, and agentic workflows across diverse real-world scenarios.

Model Overview

The table and diagram below summarize the model’s specifications and architecture.

Architecture	Hybrid-linear MoE
Parameter Scale	Total 560B, Activated 25B
Transformer Layers	63 KDA + 9 Gated MLA (7:1)
Number of Dense Layers	4
Number of Routed Experts	512
Number of Shared Experts	1
Number of Activated Experts	8
Attention Heads	64
Hidden Size	4096
Expert Intermediate Size	1280
Dense Intermediate Size	10240
Vocabulary Size	157184

Quickstart

SGLang (Recommended)

The day-0 SGLang image includes Ling-3.1-flash support, MTP (NEXTN) speculative decoding, and Hopper decode optimizations:

Run Inference

Server (8 × H200, TP8 + EP8):

bash

docker run --gpus all --network host --ipc=host --rm \  lmsysorg/sglang:dev-ling-3.1-flash \  python3 -m sglang.launch_server \    --model-path inclusionAI/Ling-3.1-flash \    --tp 8 --ep 8 \    --speculative-algorithm NEXTN \    --host 0.0.0.0 --port 30000

On 4 × GB300, use --tp 4 --ep 4 instead. Hopper decode optimizations (optional, ~10% faster batch-1 decode) can be enabled with SGLANG_ENABLE_FP8_BLOCK_GEMV=1 and SGLANG_ENABLE_FUSED_KDA_OUTPUT_QUANT=1.

Client

bash

curl -s http://localhost:30000/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{"model": "Ling-3.1-flash",       "messages": [{"role": "user", "content": "What is the capital of France?"}],       "chat_template_kwargs": {"enable_thinking": true},       "stream": true,       "temperature": 1.0,       "top_k": 20,       "top_p": 0.95}'

Verified recipes for H200, GB300, H800, H100, and B200, plus HiCache (Mooncake L3), reasoning on/off, tool calling, and measured accuracy/speed numbers, are in the SGLang Ling-3.1-flash cookbook.

vLLM

Environment Preparation

bash

pip install uv
uv venv ~/my_ling_env
source ~/my_ling_env/bin/activate
git clone -b vllm https://github.com/vllm-project/vllm.git
cd vllm
VLLM_USE_PRECOMPILED=1 uv pip install --editable . --torch-backend=auto

Run Inference

Server

bash

vllm serve "$MODEL_PATH" \    --port "$PORT" \    --trust-remote-code \    --served-model-name auto \    --tensor-parallel-size 8 \    --gpu-memory-utilization 0.85 \    --enable-prefix-caching \    --enable-expert-parallel \    --mamba-cache-mode align \    --enable-auto-tool-choice \    --tool-call-parser ling3 \    --reasoning-parser ling3 \    --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

Client
It is recommended to use the sampling parameters (temperature=1.0, top_p=0.95, top_k=20) and set enable_thinking to true for better performance.

bash

curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \  -H "Content-Type: application/json" \  -d '{"model": "auto",        "messages": [{"role": "user", "content": "What is the capital of France?"}],       "chat_template_kwargs": {"enable_thinking": true},       "stream": true,       "temperature": 1.0,        "top_k": 20,       "top_p": 0.95}'

Training Content Summary

The public training-content summary identifying Ling-3.1-flash is available below. Please refer to the document for its covered model versions, training-content scope, summary version, and update date.

This summary concerns training-content disclosure; it does not replace the model’s technical documentation, usage terms, or license.