Update README.md (#4)

- Update README.md (c0dafa941f3360797cec74f2d0ca867a8f00abcc)


Co-authored-by: Ye Zhenjie <thinkthinking@users.noreply.huggingface.co>

README.md

@@ -1,24 +1,20 @@
 ---
 license: mit
 base_model:
-- inclusionAI/Ling-mini-base-2.0
+  - inclusionAI/Ling-mini-base-2.0
 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></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🐙 <a href="https://zenmux.ai/inclusionai/ling-mini-2.0?utm_source=hf_inclusionAI">Experience Now</a></p>
 
 ## Introduction
 
-Today, we are excited to announce the open-sourcing of __Ling 2.0__ — a family of MoE-based large language models that combine __SOTA performance__ with __high efficiency__.
-The first released version, Ling-mini-2.0, is compact yet powerful. It has __16B total parameters__, but only __1.4B__ are activated per input token (non-embedding 789M). Trained on more than __20T tokens__ of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-mini-2.0 achieves remarkable improvements in complex reasoning and instruction following. With just 1.4B activated parameters, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models.
+Today, we are excited to announce the open-sourcing of **Ling 2.0** — a family of MoE-based large language models that combine **SOTA performance** with **high efficiency**.
+The first released version, Ling-mini-2.0, is compact yet powerful. It has **16B total parameters**, but only **1.4B** are activated per input token (non-embedding 789M). Trained on more than **20T tokens** of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-mini-2.0 achieves remarkable improvements in complex reasoning and instruction following. With just 1.4B activated parameters, it still reaches the top-tier level of sub-10B dense LLMs and even matches or surpasses much larger MoE models.
 
 <p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/2NKZS5LVXzcAAAAASBAAAAgADkZ7AQFr/fmt.webp" /></p>
 
@@ -28,25 +24,24 @@ We evaluated Ling-mini-2.0 on challenging general reasoning tasks in coding (Liv
 
 ### 7× Equivalent Dense Performance Leverage
 
-Guided by [Ling Scaling Laws](https://arxiv.org/abs/2507.17702), Ling 2.0 adopts a __1/32 activation ratio__ MoE architecture, with empirically optimized design choices in expert granularity, shared expert ratio, attention ratio, aux-loss free + sigmoid routing strategy, MTP loss, QK-Norm, half RoPE, and more. This enables small-activation MoE models to achieve over __7× equivalent dense performance__. In other words, __Ling-mini-2.0 with only 1.4B activated parameters (non-embedding 789M) can deliver performance equivalent to a 7–8B dense model__.
+Guided by [Ling Scaling Laws](https://arxiv.org/abs/2507.17702), Ling 2.0 adopts a **1/32 activation ratio** MoE architecture, with empirically optimized design choices in expert granularity, shared expert ratio, attention ratio, aux-loss free + sigmoid routing strategy, MTP loss, QK-Norm, half RoPE, and more. This enables small-activation MoE models to achieve over **7× equivalent dense performance**. In other words, **Ling-mini-2.0 with only 1.4B activated parameters (non-embedding 789M) can deliver performance equivalent to a 7–8B dense model**.
 
 ### High-speed Generation at 300+ token/s
 
 <p align="center"><img src="https://mdn.alipayobjects.com/huamei_fi95qp/afts/img/bnxIRaK9tzcAAAAAgSAAAAgADkZ7AQFr/original" /></p>
 
-The highly sparse small-activation MoE architecture also delivers significant training and inference efficiency. In simple QA scenarios (within 2000 tokens), __Ling-mini-2.0 generates at 300+ token/s (on H20 deployment)__ — more than __2× faster__ than an 8B dense model. Ling-mini-2.0 is able to handle __128K context length__ with YaRN, as sequence length increases, the relative speedup can reach __over 7×__.
+The highly sparse small-activation MoE architecture also delivers significant training and inference efficiency. In simple QA scenarios (within 2000 tokens), **Ling-mini-2.0 generates at 300+ token/s (on H20 deployment)** — more than **2× faster** than an 8B dense model. Ling-mini-2.0 is able to handle **128K context length** with YaRN, as sequence length increases, the relative speedup can reach **over 7×**.
 
 <p align="center"><img src="https://raw.githubusercontent.com/inclusionAI/Ling-V2/refs/heads/main/figures/needle_in_a_haystack.webp" /></p>
 
 ### Open-sourced FP8 Efficient Training Solution
 
-Ling 2.0 employs __FP8 mixed-precision training__ throughout. Compared with BF16, experiments with over 1T training tokens show nearly identical loss curves and downstream benchmark performance. To support the community in efficient continued pretraining and fine-tuning under limited compute, we are also open-sourcing our __FP8 training solution__. Based on tile/blockwise FP8 scaling, it further introduces FP8 optimizer, FP8 on-demand transpose weight, and FP8 padding routing map for extreme memory optimization. On 8/16/32 80G GPUs, compared with LLaMA 3.1 8B and Qwen3 8B, __Ling-mini-2.0 achieved 30–60% throughput gains with MTP enabled, and 90–120% throughput gains with MTP disabled__.
+Ling 2.0 employs **FP8 mixed-precision training** throughout. Compared with BF16, experiments with over 1T training tokens show nearly identical loss curves and downstream benchmark performance. To support the community in efficient continued pretraining and fine-tuning under limited compute, we are also open-sourcing our **FP8 training solution**. Based on tile/blockwise FP8 scaling, it further introduces FP8 optimizer, FP8 on-demand transpose weight, and FP8 padding routing map for extreme memory optimization. On 8/16/32 80G GPUs, compared with LLaMA 3.1 8B and Qwen3 8B, **Ling-mini-2.0 achieved 30–60% throughput gains with MTP enabled, and 90–120% throughput gains with MTP disabled**.
 
 ### A More Open Opensource Strategy
 
 We believe Ling-mini-2.0 is an ideal starting point for MoE research. For the first time at this scale, it integrates 1/32 sparsity, MTP layers, and FP8 training — achieving both strong effectiveness and efficient training/inference performance, making it a prime candidate for the small-size LLM segment.
-To further foster community research, in addition to releasing the post-trained version, we are also open-sourcing __five pretraining checkpoints__: the pre-finetuning Ling-mini-2.0-base, along with four base models trained on 5T, 10T, 15T, and 20T tokens, enabling deeper research and broader applications.
-
+To further foster community research, in addition to releasing the post-trained version, we are also open-sourcing **five pretraining checkpoints**: the pre-finetuning Ling-mini-2.0-base, along with four base models trained on 5T, 10T, 15T, and 20T tokens, enabling deeper research and broader applications.
 
 ## Model Downloads
 
@@ -55,30 +50,65 @@ You can download the following table to see the various stage of Ling-mini-2.0 m
 <center>
 
 |       **Model**        | **Context Length** |                                                                             **Download**                                                                             |
-|:----------------------:| :----------------: |:--------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
-|   Ling-mini-base-2.0   |        32K -> 128K (YaRN)         |     [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0)     |
-| Ling-mini-base-2.0-5T  |        4K         |  [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-5T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-5T)  |
-| Ling-mini-base-2.0-10T |        4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-10T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-10T) |
-| Ling-mini-base-2.0-15T |        4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-15T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-15T) |
-| Ling-mini-base-2.0-20T |        4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-20T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-20T) |
-|     Ling-mini-2.0      |        32K -> 128K (YaRN)        |          [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-2.0)          |
+| :--------------------: | :----------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   Ling-mini-base-2.0   | 32K -> 128K (YaRN) |     [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0)     |
+| Ling-mini-base-2.0-5T  |         4K         |  [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-5T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-5T)  |
+| Ling-mini-base-2.0-10T |         4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-10T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-10T) |
+| Ling-mini-base-2.0-15T |         4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-15T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-15T) |
+| Ling-mini-base-2.0-20T |         4K         | [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-base-2.0-20T) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-base-2.0-20T) |
+|     Ling-mini-2.0      | 32K -> 128K (YaRN) |          [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ling-mini-2.0) <br>[🤖 ModelScope](https://www.modelscope.cn/models/inclusionAI/Ling-mini-2.0)          |
 
 </center>
 
 Note: If you are interested in previous version, please visit the past model collections in [Huggingface](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
 
-
 ## Quickstart
 
+### 🚀 Try Online
+
+You can experience Ling-mini-2.0 online at: [ZenMux](https://zenmux.ai/inclusionai/ling-mini-2.0?utm_source=hf_inclusionAI)
+
+### 🔌 API Usage
+
+You can also use Ling-mini-2.0 through API calls:
+
+```python
+from openai import OpenAI
+
+# 1. Initialize the OpenAI client
+client = OpenAI(
+    # 2. Point the base URL to the ZenMux endpoint
+    base_url="https://zenmux.ai/api/v1",
+    # 3. Replace with the API Key from your ZenMux user console
+    api_key="<your ZENMUX_API_KEY>",
+)
+
+# 4. Make a request
+completion = client.chat.completions.create(
+    # 5. Specify the model to use in the format "provider/model-name"
+    model="inclusionai/ling-mini-2.0",
+    messages=[
+        {
+            "role": "user",
+            "content": "What is the meaning of life?"
+        }
+    ]
+)
+
+print(completion.choices[0].message.content)
+```
+
 ### Convert to safetensors
 
 Models with safetensors format can be downloaded from [HuggingFace](https://huggingface.co/inclusionAI) or [ModelScope](https://modelscope.cn/organization/inclusionAI).
 If you want to train your model and eval it, you can convert from dcp produced by training.
+
 ```shell
 python tools/convert_dcp_to_safe_tensors.py --checkpoint-path ${DCP_PATH} --target-path ${SAFETENSORS_PATH}
 ```
 
 Currently, BF16 and FP8 formats are supported, you can use convert parameter to handle it:
+
 - `--force-bf16` for BF16 format.
 - `--force-fp8` for FP8 format.
 
@@ -88,9 +118,7 @@ Here is a code snippet to show you how to use the chat model with `transformers`
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-
 model_name = "inclusionAI/Ling-mini-2.0"
-
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     dtype="auto",
@@ -98,7 +126,6 @@ model = AutoModelForCausalLM.from_pretrained(
     trust_remote_code=True,
 )
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-
 prompt = "Give me a short introduction to large language models."
 messages = [
     {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
@@ -110,7 +137,6 @@ text = tokenizer.apply_chat_template(
     add_generation_prompt=True
 )
 model_inputs = tokenizer([text], return_tensors="pt", return_token_type_ids=False).to(model.device)
-
 generated_ids = model.generate(
     **model_inputs,
     max_new_tokens=512
@@ -118,7 +144,6 @@ generated_ids = model.generate(
 generated_ids = [
     output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
 ]
-
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 
@@ -149,25 +174,20 @@ pip install -e .
 ```bash
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
-
 tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ling-mini-2.0")
-
 sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)
-
 llm = LLM(model="inclusionAI/Ling-mini-2.0", dtype='bfloat16')
 prompt = "Give me a short introduction to large language models."
 messages = [
     {"role": "system", "content": "You are Ling, an assistant created by inclusionAI"},
     {"role": "user", "content": prompt}
 ]
-
 text = tokenizer.apply_chat_template(
     messages,
     tokenize=False,
     add_generation_prompt=True
 )
 outputs = llm.generate([text], sampling_params)
-
 ```
 
 #### Online Inference:
@@ -181,7 +201,9 @@ vllm serve inclusionAI/Ling-mini-2.0 \
 ```
 
 To handle long context in vLLM using YaRN, we need to follow these two steps:
+
 1. Add a `rope_scaling` field to the model's `config.json` file, for example:
+
 ```json
 {
   ...,
@@ -192,24 +214,29 @@ To handle long context in vLLM using YaRN, we need to follow these two steps:
   }
 }
 ```
+
 2. Use an additional parameter `--max-model-len` to specify the desired maximum context length when starting the vLLM service.
 
 For detailed guidance, please refer to the vLLM [`instructions`](https://docs.vllm.ai/en/latest/).
 
-
 ### SGLang
 
 #### Environment Preparation
 
 We will later submit our model to SGLang official release, now we can prepare the environment following steps:
+
 ```shell
 pip3 install sglang==0.5.2rc0 sgl-kernel==0.3.7.post1
 ```
+
 You can use docker image as well:
+
 ```shell
 docker pull lmsysorg/sglang:v0.5.2rc0-cu126
 ```
+
 Then you should apply patch to sglang installation:
+
 ```shell
 # patch command is needed, run `yum install -y patch` if needed
 patch -d `python -c 'import sglang;import os; print(os.path.dirname(sglang.__file__))'` -p3 < inference/sglang/bailing_moe_v2.patch
@@ -217,9 +244,10 @@ patch -d `python -c 'import sglang;import os; print(os.path.dirname(sglang.__fil
 
 #### Run Inference
 
-BF16 and FP8 models are supported by SGLang now, it depends on the dtype of the model in ${MODEL_PATH}. They both share the same command in the following:  
+BF16 and FP8 models are supported by SGLang now, it depends on the dtype of the model in ${MODEL_PATH}. They both share the same command in the following:
 
 - Start server:
+
 ```shell
 python -m sglang.launch_server \
     --model-path $MODLE_PATH \
@@ -227,16 +255,19 @@ python -m sglang.launch_server \
     --trust-remote-code \
     --attention-backend fa3
 ```
+
 MTP is supported for base model, and not yet for chat model. You can add parameter `--speculative-algorithm NEXTN`
 to start command.
 
 - Client:
+
 ```shell
 curl -s http://localhost:${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.ai/basic_usage/send_request.html)
 
 ## Training
@@ -254,11 +285,11 @@ The table below shows the pre-training performance of several models, measured i
 <center>
 
 |        **Model**        | **8 x 80G GPUs (GBS=128)** | **16 x 80G GPUs (GBS=256)** | **32 x 80G GPUs (GBS=512)** |
-|:-----------------------:| :--------------------: | :---------------------: | :---------------------: |
-| LLaMA 3.1 8B (baseline) |         81222          |         161319          |         321403          |
-|        Qwen3 8B         |    55775 (-31.33%)     |    109799 (-31.94%)     |    219943 (-31.57%)     |
-|      Ling-mini-2.0      |    109532 (+34.86%)    |    221585 (+37.36%)     |    448726 (+39.61%)     |
-|  Ling-mini-2.0 w/o MTP  |    128298 (+57.96%)    |    307264 (+90.47%)     |    611466 (+90.25%)     |
+| :---------------------: | :------------------------: | :-------------------------: | :-------------------------: |
+| LLaMA 3.1 8B (baseline) |           81222            |           161319            |           321403            |
+|        Qwen3 8B         |      55775 (-31.33%)       |      109799 (-31.94%)       |      219943 (-31.57%)       |
+|      Ling-mini-2.0      |      109532 (+34.86%)      |      221585 (+37.36%)       |      448726 (+39.61%)       |
+|  Ling-mini-2.0 w/o MTP  |      128298 (+57.96%)      |      307264 (+90.47%)       |      611466 (+90.25%)       |
 
 </center>