Create README.md

README.md

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+---
+license: mit
+pipeline_tag: text-generation
+library_name: transformers
+---
+<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; | &nbsp;&nbsp;🐙 Experience Link Coming Soon~</p>
+<!-- <a href="https://zenmux.ai/inclusionai/ling-1t?utm_source=hf_inclusionAI">Experience Now</a> -->
+
+## Ling-2.6-1T: A Trillion-Parameter Comprehensive Flagship Model for Complex Tasks
+
+Today, we are thrilled to open-source **Ling–2.6–1T** from the Ling family.
+
+Tailored for real–world, complex scenarios, this trillion–parameter model introduces targeted optimizations across inference efficiency, token overhead, and agentic capabilities, making it highly effective for **coding and daily workflows**.
+
+Key upgrades in **Ling–2.6–1T** include:
+
+* **High Inference Efficiency:** By adopting a hybrid architecture combining **MLA and Linear Attention**, we dramatically reduce latency and VRAM footprint for long contexts. It delivers superior throughput and lower per–token computational costs without sacrificing expressivity, ensuring real–time responsiveness for complex reasoning and tool calling.
+* **Lower Token Overhead via "Fast Thinking":** We introduce a *Contextual Process Redundancy Suppression* reward strategy during post–training. This reduces reliance on verbose chains–of–thought (CoT), utilizing a "fast thinking" mechanism to reach answers directly and compress output costs while maintaining top–tier intelligence.
+* **Reliable Multi–Step Execution:** With enhanced reasoning, agentic coding, and instruction following, Ling–2.6–1T achieves **open–source SOTA** on execution–heavy benchmarks, including AIME26, SWE–bench Verified, BFCL–V4, TAU2–Bench, and IFBench.
+* **Production–Ready for Agent Workflows:** Designed for end–to–end engineering—from code generation to bug fixing—Ling–2.6–1T integrates seamlessly with mainstream agent frameworks like *Claude Code, OpenClaw, OpenCode, and CodeBuddy*, effortlessly handling multi–tool, multi–step constraints in enterprise environments.
+
+
+### **Unlocking Robust Intelligence with Superior Efficiency**
+On [Artificial Analysis](https://artificialanalysis.ai/), **Ling-2.6-1T** achieved an **Intelligence Index of 34** with approximately 16M output tokens, representing a significant generational leap over the previous Ling-1T. This positioning underscores its ability to deliver high-tier intelligence with optimized token consumption.
+
+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_fst7or/afts/img/48cCTY8XJgUAAAAAZvAAAAgADpRXAQJr/original" />
+</p>
+
+
+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_fst7or/afts/img/AmTNT5tQHDYAAAAAaSAAAAgADpRXAQJr/original " width="48%"/>
+    <img src="https://mdn.alipayobjects.com/huamei_fst7or/afts/img/Wv_8Toxbl7IAAAAAaRAAAAgADpRXAQJr/original"  width="48%"/>
+</p>
+
+
+### **Enhancing Execution Stability for Complex Multi-Step Tasks**
+
+Ling-2.6-1T demonstrates balanced excellence across reasoning, coding, and tool-calling, achieving **open-source SOTA** status on multiple execution-heavy benchmarks:
+
+*   **Advanced Reasoning:** Significantly leads non-thinking models on *AIME26*, showcasing superior complex problem-solving capabilities.
+*   **First-Tier Agent Execution:** Ranks among the top models on *SWE-bench Verified, TAU2-Bench, Claw-Eval, BFCL-V4, and PinchBench*, proving high reliability in real-world workflows.
+*   **Context & Constraints:** Strong performance on *MRCR (16K–256K)* and *IFBench* ensures logical consistency and precision under complex instructions and long contexts.
+
+<p align="center">
+    <img src="https://mdn.alipayobjects.com/huamei_fst7or/afts/img/Ykl9QZamkj0AAAAAgBAAAAgADpRXAQJr/original" />
+</p>
+
+
+## Model Downloads
+
+You can download Ling-2.6-1T from the following table. If you are located in mainland China, we also provide the model on ModelScope.cn to speed up the download process.
+
+<center>
+
+|  **Model**  | **Context Length** |                                                                     **Download**                                                                      |
+| :---------: | :----------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Ling-2.6-1T | 256K -> 1M (YaRN) | [🤗 HuggingFace]() &nbsp;&nbsp; [🤖 ModelScope]() |
+</center>
+
+Note: If you are interested in the previous version, please visit the past model collections on [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
+
+## Quickstart
+
+### 🚀 Try Online
+
+Coming Soon
+
+### 🔌 API Usage
+
+https://openrouter.ai/inclusionai/ling-2.6-1t:free
+
+
+## Deployment
+
+
+### SGLang
+
+#### Environment Preparation
+
+```shell
+pip install uv
+
+uv venv ~/my_ling_env
+
+source ~/my_ling_env/bin/activate
+
+# uv pip "sglang-kernel>=0.4.1"
+uv pip install "sglang[all]>=0.5.10.post1" --prerelease=allow
+```
+
+#### Run Inference
+
+Here is the example to run Ling-1T with 8 GPUs, where the server port is ${PORT}:
+
+**Server**
+
+**1. Standard Inference (Without MTP)**
+```bash
+sglang serve \
+  --model-path inclusionAI/Ling-2.6-1T \
+  --tp-size 8 \
+  --max-running-requests 32 \
+  --mem-fraction-static 0.92 \
+  --chunked-prefill-size 8192 \
+  --context-length 262144 \
+  --trust-remote-code \
+  --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \
+  --tool-call-parser qwen25
+```
+
+**2. Inference with MTP (Multi-Token Prediction)**  
+_The current official SGLang implementation of MTP contains a bug. For better inference performance, we recommend installing our patched version. Our fix is currently under review and is expected to be merged into the official SGLang library shortly._
+
+**Install our SGLang**
+```bash
+git clone -b ling_2_6 git@github.com:antgroup/sglang.git
+cd sglang
+
+pip install --upgrade pip
+pip install -e "python"
+```
+Start server
+```bash
+sglang serve \
+  --model-path inclusionAI/Ling-2.6-1T \
+  --tp-size 8 \
+  --max-running-requests 32 \
+  --mem-fraction-static 0.92 \
+  --chunked-prefill-size 8192 \
+  --context-length 262144 \
+  --trust-remote-code \
+  --speculative-algorithm EAGLE \
+  --speculative-num-steps 3 \
+  --speculative-eagle-topk 1 \
+  --speculative-num-draft-tokens 4 \
+  --mamba-scheduler-strategy extra_buffer \
+  --mamba-full-memory-ratio 1.4 \
+  --model-loader-extra-config '{"enable_multithread_load":"true","num_threads":64}' \
+  --tool-call-parser qwen25
+```
+
+**Client**
+
+```bash
+curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}'
+```
+
+More usage can be found [here](https://docs.sglang.io/cookbook/autoregressive/InclusionAI/Ling-2.6#3-2-ling-2-6-1t)
+
+#### vLLM
+##### Environment Preparation
+```bash
+pip install uv
+
+uv venv ~/my_ling_env
+
+source ~/my_ling_env/bin/activate
+
+git clone 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 $PORT \
+    --served-model-name my_model \
+    --trust-remote-code --tensor-parallel-size 8 \
+    --gpu-memory-utilization 0.85
+```
+
+**Client**
+
+```bash
+curl -s http://${MASTER_IP}:${PORT}/v1/chat/completions \
+  -H "Content-Type: application/json" \
+  -d '{"model": "auto", "messages": [{"role": "user", "content": "What is the capital of France?"}]}'
+```
+
+
+## Limitations & Future Plans
+
+While Ling-2.6-1T excels in reasoning and agentic efficiency, our future development will focus on:
+
+*   **Intelligence-Efficiency Balance:** Further optimizing token efficiency for knowledge-intensive tasks.
+*   **Long-Range Consistency:** Enhancing global consistency in long-term planning and complex information retrieval.
+*   **Dynamic Alignment:** Refining cross-lingual alignment to eliminate occasional language-switching offsets under complex instructions.
+
+We remain committed to pushing the boundaries of model performance to enhance delivery efficiency across all complex scenarios.
+
+## License
+
+This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ling-V2/blob/main/LICENSE).