🤗 Hugging Face | 🤖 ModelScope | 🐙 OpenRouter
Introduction
We are introducing Ling-3.0-tiny, a lightweight hybrid reasoning MoE model with 7.9B total parameters and only 1.3B activated parameters per token. It is designed to deliver strong reasoning and agentic capabilities at low inference cost, making advanced model capabilities more accessible for local and resource-constrained deployment. BF16, FP8, and INT4 weights are provided for a wide range of hardware and deployment settings.
Key highlights of the model are summarized below:
- Efficient Hybrid-Linear Architecture: Ling-3.0-tiny integrates a 3:1 alternating stacking of KDA and MLA (3 Kimi Delta Attention layers followed by 1 Multi-Head Latent Attention layer per 4-layer block) with a sparse MoE FFN comprising 128 routed experts. Each token activates only 8 routed experts and 1 shared expert, allowing the model to balance long-context modeling capability, parameter efficiency, and computational cost.
- Native Hybrid Reasoning and Agentic Capabilities: Ling-3.0-tiny supports both fast responses and multi-step reasoning, with thinking mode configurable per request through
enable_thinking. It delivers balanced performance across general agent tasks, coding, mathematical and scientific reasoning, and instruction following. - Local and Edge Deployment: Designed for efficient local deployment, Ling-3.0-tiny has been validated on NVIDIA DGX Spark, Apple Silicon MacBook, and Mac mini, enabling capable reasoning and agentic workloads without datacenter-class GPUs. With FP8, Ling-3.0-tiny reaches around 100-105 tokens/s on DGX Spark and 86-90 tokens/s on an M4 Pro MacBook, with approximately 8.34 GiB peak memory usage at an 8K context length.
Model Overview
Ling-3.0-tiny inherits the hybrid linear attation architecture of Ling-3.0 series, while being specifically optimized for lightweight and accessible deployment. The model has 7.9B total parameters, with only 1.3B parameters activated per token.
The architecture of Ling-3.0-tiny is designed to make computational efficiency serve real-world agentic performance.
- A 3:1 KDA–MLA architecture (3 KDA layers and 1 MLA layer per 4-layer block) provides more efficient long-context processing;
- A sparse MoE FFN with 128 experts activates 8 routed experts and 1 shared expert per token, enabling broad model capabilities with only 1.3B activated parameters per token.
- Native hybrid reasoning enables fast responses for routine tasks and multi-step reasoning for complex tasks within a single model.
Overall, these designs deliver the inference efficiency needed to deploy lightweight models in real-world agentic workflows.
Evaluation
We evaluated Ling-3.0-tiny across agentic tasks, coding, long-context understanding, knowledge reliability, mathematical and scientific reasoning, and instruction following. Ling-3.0-tiny achieves a score of 25 on the Artificial Analysis Intelligence Index v4.1.1 and 16 on the Artificial Analysis Agentic Index. In Artificial Analysis testing, Ling-3.0-tiny reaches an output speed of over 160 tokens/s, with approximately 18 seconds of end-to-end latency for a 500-token response, including reasoning time. These results highlight the model's efficiency relative to its 1.3B activated parameter footprint.
The following table presents representative benchmarks for Ling-3.0-tiny:
- Thinking mode is enabled by default. The recommended sampling parameters for Ling-3.0-tiny are
temperature=1.0,top_p=0.95, andtop_k=20.- 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 uses temperature=1.0, max_new_tokens=32K, with a 256K context window.
Quickstart
SGLang
The hardware- and recipe-specific launch matrix (BF16/FP8 × Low-Latency / High-Throughput / HiCache + Mooncake), with a live command generator and verified configurations, lives in the SGLang cookbook:
Cookbook: https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-3.0-tiny
Install SGLang
Use the pre-built image that tracks the Ling-3.0 runtime:
docker pull lmsysorg/sglang:dev-Ling-3.0-tiny
Run Inference
Recommended low-latency recipe (built-in MTP / NEXTN, 256K YaRN context) on 1× 141GB-class GPU (H20-3e) or a 1-GPU Blackwell node:
Server
docker run --rm --gpus all --ipc=host --shm-size 32g \
-p 30000:30000 \
-e HF_TOKEN=<your-hf-token> \
lmsysorg/sglang:dev-Ling-3.0-tiny \
env SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
python3 -m sglang.launch_server \
--model-path inclusionAI/Ling-3.0-tiny \
--tp 1 \
--json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":2.0,"rope_theta":6000000,"partial_rotary_factor":0.5,"original_max_position_embeddings":131072}}' \
--context-length 262144 \
--speculative-algorithm NEXTN \
--mem-fraction-static 0.8 \
--host 0.0.0.0 \
--port 30000
Client
Thinking is enabled by default by both the chat template and the ling3 reasoning parser. Disable it per request with "chat_template_kwargs": {"enable_thinking": false}. We recommend the sampling parameters temperature=1.0, top_p=0.95, and top_k=20.
curl -s http://localhost:30000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "auto",
"messages": [{"role": "user", "content": "What is the capital of France?"}],
"stream": true,
"temperature": 1.0,
"top_k": 20,
"top_p": 0.95
}'
For --reasoning-parser ling3 / --tool-call-parser ling3, the HiCache + Mooncake L3 setup, and GSM8K / bench_serving reproduction commands, see the cookbook page linked above.
vLLM
Install vLLM with Ling-3.0 Support
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
Run Inference
Here is the example to run Ling-3.0-tiny with a single GPU, where the server port is ${PORT}:
Server
vllm serve "$MODEL_PATH" \
--port "$PORT" \
--trust-remote-code \
--served-model-name auto \
--tensor-parallel-size 1 \
--gpu-memory-utilization 0.85 \
--enable-prefix-caching \
--mamba-cache-mode align \
--enable-auto-tool-choice \
--tool-call-parser ling3 \
--reasoning-parser ling3
Client
For better performance, We recommend setting enable_thinking=true with temperature=1.0, top_p=0.95, and top_k=20.
curl -s http://${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
}'
Ollama
- This configuration has been verified on an M4 Pro Mac with 48 GB of unified memory.
Preparation and Build
git clone https://github.com/ollama/ollama.git
cd ollama
git fetch origin refs/pull/17643/head:bailing-moe-v3
git switch bailing-moe-v3
cmake -B build .
cmake --build build --parallel 8
- Support is currently provided by ollama/ollama#17643 and is limited to running via MLX on Apple Silicon.
- Use the local
./ollamaexecutable built from source in this section. This functionality is not yet included in the official Ollama release.
Import Model
Replace /absolute/path/to/fp8_weights with the absolute path to the FP8 model weights directory. The imported model will be named ling-tiny-fp8:
printf 'FROM /absolute/path/to/fp8_weights\n' > /tmp/Modelfile.ling
./ollama create ling-tiny-fp8 --experimental -f /tmp/Modelfile.ling
Start Service
Set the default context length to 8192, and then start the Ollama service:
# The service listens on http://127.0.0.1:11434 by default
OLLAMA_CONTEXT_LENGTH=8192 ./ollama serve
Call API
curl -sS http://127.0.0.1:11434/api/generate -d '{
"model": "ling-tiny-fp8",
"prompt": "<role>SYSTEM</role>detailed thinking on<|role_end|><role>HUMAN</role>Calculate 17 × 23 and output only the number.<|role_end|><role>ASSISTANT</role>\n<think>",
"raw": true,
"think": true,
"stream": false,
"options": {
"temperature": 1.0,
"top_p": 0.95,
"top_k": 20,
"num_predict": 2048
}
}' | jq -r .response