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README.md

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+
 ---
 license: mit
+pipeline_tag: text-generation
 ---
+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4QxcQrBlTiAAAAAAQXAAAAgAemJ7AQ/original" width="100"/>
+</p>
+<p align="center">🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>&nbsp;&nbsp; | &nbsp;&nbsp;🤖 <a href="https://modelscope.cn/organization/inclusionAI">ModelScope </a>&nbsp;&nbsp;</p>
+
+# Introduction
+We are introducing Ling-3.0-flash-VL, our next-generation native multimodal model. Built upon Ling-3.0-flash, it brings visual information into the complete process of understanding, reasoning, acting, and verification—advancing beyond image and video perception to solving real-world tasks through vision.
+With 124B total parameters, only 5.5B activated parameters per token, support for image and video inputs, and a context window of up to 1M tokens, Ling-3.0-flash-VL delivers powerful multimodal reasoning and agentic capabilities with exceptional efficiency.
+
+# Model Overview
+Ling-3.0-flash-VL inherits the language, reasoning, and long-context capabilities of Ling-3.0-flash, while extending them with native image and video understanding. The model has 124B total parameters, with only 5.5B parameters activated per token, and supports a context window of up to 1M tokens.
+
+The architecture of Ling-3.0-flash-VL is designed to integrate visual information into real-world reasoning and agentic workflows.
+
+- A ViT visual encoder extracts features from images and videos, while a two-layer MLP projector aligns visual features with text representations for unified multimodal understanding and reasoning;
+- VideoRoPE encodes both spatial positions and temporal order, enabling the model to understand visual changes over time and supporting tasks such as event localization, long-video question answering, and video clip editing;
+- A 42-layer hybrid backbone alternates KDA and Gated MLA layers at a 5:1 ratio, enabling efficient long-context processing across text, images, videos, and extended agent task histories;
+- A sparse MoE architecture maintains a total model capacity of 124B parameters while activating only 5.5B parameters per token, balancing strong multimodal capabilities with inference efficiency.
+
+Overall, these designs make vision more than just an input, integrating it into the complete process of understanding, reasoning, planning, acting, and verification.
+
+
+![ling-3.0-flash-vl-0906](https://cdn-uploads.huggingface.co/production/uploads/6666ca359f5a0b3229238a1a/WhsE3cM7QMelBCjdeyex2.png)
+
+# Evaluation
+Ling-3.0-flash-VL achieves a score of **42** on the Artificial Analysis Intelligence Index v4.1.1, improving by 4 points over Ling-3.0-flash’s score of 38. The results show that extending the model with visual capabilities further improves its overall intelligence performance.
+
+
+![ling-3.0-flash-vl-aa](https://cdn-uploads.huggingface.co/production/uploads/6666ca359f5a0b3229238a1a/5gqrYjboQ8j7sTWjd1A_q.png)
+
+Across multimodal benchmarks, Ling-3.0-flash-VL demonstrates three distinct capability dimensions:
+
+- **Understand: Comprehending complex visual information.** The model can handle object counting, complex layouts, charts, and document content.
+- **Reason: Reasoning and verification with visual evidence.** The model can use visual information for calculation, multi-step reasoning, and external information verification.
+- **Act: Interacting with interfaces and completing tasks.** The model can understand web and software interfaces, then translate visual information into sequences of actions.
+
+![ling-3.0-flash-vl-benchmark](https://cdn-uploads.huggingface.co/production/uploads/6666ca359f5a0b3229238a1a/w-V0gtCa3qy0un6vuz-sj.png)
+
+> + Thinking mode is enabled by default. Unless otherwise specified, the default parameters for Ling-3.0-flash-VL are as follows: `temperature=0.6`, `top_p=0.95`, `top_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), with a live command generator and verified configurations, lives in the SGLang cookbook:
+
+**Cookbook:** https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-3.0-flash-VL
+
+
+### Install SGLang
+
+```bash
+docker pull lmsysorg/sglang:dev-Ling-3.0-flash-VL
+```
+
+### Run Inference
+Recommended recipe with 256K context (YaRN), on 4× 141GB-class GPUs (H20-3e / H200) or 4-GPU Blackwell nodes (B300 / GB300):
+
+```bash
+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-flash-VL \
+  env SGLANG_ALLOW_OVERWRITE_LONGER_CONTEXT_LEN=1 \
+  python3 -m sglang.launch_server \
+    --model-path inclusionAI/Ling-3.0-flash-VL \
+    --tp 4 \
+    --context-length 262144 \
+    --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}}' \
+    --mem-fraction-static 0.85 \
+    --trust-remote-code \
+    --reasoning-parser auto \
+    --tool-call-parser auto \
+    --host 0.0.0.0 \
+    --port 30000
+```
+
+On 80GB cards (H100 / H800), scale out to `--tp 8`. The reasoning and tool-call parsers resolve automatically to `ling3` from the chat template; you can also set them explicitly with `--reasoning-parser ling3 --tool-call-parser ling3`.
+
+**Client**
+
+Thinking is enabled by default by the chat template; disable it per request with `"chat_template_kwargs": {"enable_thinking": false}`. Recommended sampling: `temperature=1.0`, `top_p=0.95`, `top_k=20` (per `generation_config.json`).
+
+```bash
+curl -s http://localhost:30000/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "inclusionAI/Ling-3.0-flash-VL",
+       "messages": [{"role": "user", "content": [
+         {"type": "image_url", "image_url": {"url": "https://example.com/image.png"}},
+         {"type": "text", "text": "Describe this image in one sentence."}
+       ]}],
+       "stream": true,
+       "temperature": 1.0, "top_k": 20, "top_p": 0.95
+     }'
+```
+Video input uses `{"type": "video_url", "video_url": {"url": "..."}}` in the same message shape. For MMMU-Pro / `bench_serving` reproduction commands and per-hardware recipes, see the cookbook page linked above.
+
+## vLLM
+### Environment Preparation
+
+```bash
+pip install uv
+
+uv venv ~/my_ling_env
+
+source ~/my_ling_env/bin/activate
+
+git clone 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
+
+**Server**
+
+```bash
+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
+```
+
+**Client**
+
+Thinking is enabled by default by the chat template; disable it per request with `"chat_template_kwargs": {"enable_thinking": false}`. Recommended sampling: `temperature=1.0`, `top_p=0.95`, `top_k=20` (per `generation_config.json`).
+
+```bash
+curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "auto", -d '{"model": "inclusionAI/Ling-3.0-flash-VL",
+       "messages": [{"role": "user", "content": [
+         {"type": "image_url", "image_url": {"url": "https://example.com/image.png"}},
+         {"type": "text", "text": "Describe this image in one sentence."}
+       ]}],
+       "stream": true,
+       "temperature": 1.0, "top_k": 20, "top_p": 0.95
+     }'
+```
+Video input uses `{"type": "video_url", "video_url": {"url": "..."}}` in the same message shape. For MMMU-Pro / `bench_serving` reproduction commands and per-hardware recipes, see the cookbook page linked above.