Update README.md

README.md

@@ -1,72 +1,66 @@
 ---
 license: mit
 base_model:
-- inclusionAI/Ling-flash-base-2.0
+  - inclusionAI/Ling-flash-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>
 
-
 ## Introduction
 
 Today, we are officially open-sourcing Ring-flash-2.0.
 
-This is a __high-performance thinking model, deeply optimized__ based on Ling-flash-2.0-base. Like Ling-flash-2.0, Ring-flash-2.0 has a total of 100B parameters, with only 6.1B activated per inference. Our independently developed __icepop algorithm__ has successfully addressed the challenge of training instability in reinforcement learning (RL) for MoE LLMs after cold-start Long-CoT SFT, enabling the model’s complex reasoning capabilities to continuously improve throughout extended RL training cycles.
+This is a **high-performance thinking model, deeply optimized** based on Ling-flash-2.0-base. Like Ling-flash-2.0, Ring-flash-2.0 has a total of 100B parameters, with only 6.1B activated per inference. Our independently developed **icepop algorithm** has successfully addressed the challenge of training instability in reinforcement learning (RL) for MoE LLMs after cold-start Long-CoT SFT, enabling the model’s complex reasoning capabilities to continuously improve throughout extended RL training cycles.
 
-Ring-flash-2.0 demonstrates significant breakthroughs across multiple challenging benchmarks, including __math competitions__, __code generation__, and __logical reasoning__. Its performance not only surpasses that of SOTA dense models under 40B parameters but also rivals larger open-weight MoE models and closed-source high-performance thinking model APIs.
+Ring-flash-2.0 demonstrates significant breakthroughs across multiple challenging benchmarks, including **math competitions**, **code generation**, and **logical reasoning**. Its performance not only surpasses that of SOTA dense models under 40B parameters but also rivals larger open-weight MoE models and closed-source high-performance thinking model APIs.
 
 ### leading-level performance in complex reasoning
 
-We selected __representative open-source thinking models__ and __closed-source APIs__ for comparison, including GPT-OSS-120B(medium), Qwen3-32B-Thinking, Seed-OSS-36B-Instruct, and Gemini-2.5-Flash.
+We selected **representative open-source thinking models** and **closed-source APIs** for comparison, including GPT-OSS-120B(medium), Qwen3-32B-Thinking, Seed-OSS-36B-Instruct, and Gemini-2.5-Flash.
 
 The benchmarking results demonstrate that Ring-flash-2.0 exhibits leading performance across multiple challenging general reasoning tasks, including:
-- __Math competitions__ (AIME 25, Omni-MATH),
-- __Code generation__ (LiveCodeBench, CodeForce-Elo),
-- __Logical reasoning__ (ARC-Prize).
-It also shows strong competitiveness in specialized domains such as:
-- __Scientific and medical reasoning__ (GPQA-Diamond, HealthBench).
 
-More surprisingly, although Ring-flash-2.0 is primarily designed for complex reasoning, it outperforms all other compared models in __creative writing__ (Creative Writing v3) and matches the creative capability of its "twin brother"—the non-thinking model Ling-flash-2.0.
+- **Math competitions** (AIME 25, Omni-MATH),
+- **Code generation** (LiveCodeBench, CodeForce-Elo),
+- **Logical reasoning** (ARC-Prize).
+  It also shows strong competitiveness in specialized domains such as:
+- **Scientific and medical reasoning** (GPQA-Diamond, HealthBench).
+
+More surprisingly, although Ring-flash-2.0 is primarily designed for complex reasoning, it outperforms all other compared models in **creative writing** (Creative Writing v3) and matches the creative capability of its "twin brother"—the non-thinking model Ling-flash-2.0.
+
 <p align="center">
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*jLbeS74JqB8AAAAAWmAAAAgAemJ7AQ/original"/>
 <p>
-
 <p align="center">
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*_AG2T62ZWNsAAAAAWKAAAAgAemJ7AQ/original"/>
 <p>
 
-
 ### Efficient Architecture, High-Speed Inference
 
 <p align="center">
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*awCaS4yTD9UAAAAAUdAAAAgAemJ7AQ/original"/>
 <p>
-
 Building on the highly efficient MoE architecture of the Ling 2.0 series, and through structural optimizations such as a __1/32 expert activation ratio__ and __MTP layers__, Ring-flash-2.0 activates only 6.1B (4.8B non-embedding) parameters while delivering performance comparable to a ∼40B dense model.
 Thanks to its low activation and high sparsity design, Ring-flash-2.0 achieves a high generation speed of __200+ tokens/sec__ when deployed on just four H20 GPUs, significantly reducing inference costs for thinking models in high-concurrency scenarios.
 
-
 ## IcePop: Cooling Down Training-Inference Gaps in RL for MoE Models
 
 During the RL for MoE models, the discrepancy of precision between the training and inference engines is more pronounced compared to dense models. This gap widens progressively as sequence length and training steps increase—particularly during long-sequence generation and extended training cycles. A more critical issue is that the original GRPO algorithm begins to break down within a limited number of training steps. Specifically, the probabilistic discrepancy for the same token between training and inference phases gradually increases. When this relative difference exceeds 5%, training effectively fails, posing a significant challenge for long-horizon reinforcement learning with lengthy sequences.
 
-To address this issue, we introduced a key solution: __distribution calibration via masked bidirectional truncation, which effectively narrows the gap between training and inference__.
+To address this issue, we introduced a key solution: **distribution calibration via masked bidirectional truncation, which effectively narrows the gap between training and inference**.
 
 - Bidirectional Truncation: We truncate not only tokens where the training probability is significantly higher than the inference probability but also the reverse scenario where the training probability is much lower.
 - Masking: Tokens with excessively large discrepancies are excluded from gradient computation.
 
 For detailed algorithm introduction, please refer to our technical blog: https://ringtech.notion.site/icepop
 
-
 ## SFT + RLVR + RLHF Multi-Stage Training
+
 To comprehensively enhance the capabilities of Ring-flash-2.0, we designed a Two-staged RL pipeline. First, lightweight Long-CoT SFT equips the Ling-flash-2.0-base model with diverse thinking patterns. This is followed by RL training with Verifiable Rewards (RLVR) to continually stimulate the model’s reasoning potential. Finally, an RLHF phase is incorporated to improve the model’s general abilities.
 
 During RL training, we compared directly combining RLVR and RLHF into joint training with the ultimately adopted Two-staged RL pipeline. Both approaches showed relatively similar effectiveness in our experiments. However, due to the differing difficulty levels of RLVR and RLHF tasks—with RLHF involving relatively shorter model rollouts—joint training resulted in more long-tail generations. From an engineering efficiency perspective, we ultimately adopted the Two-staged RL approach.
@@ -75,8 +69,6 @@ During RL training, we compared directly combining RLVR and RLHF into joint trai
     <img src="https://mdn.alipayobjects.com/huamei_qa8qxu/afts/img/A*4Q_4SbSv73YAAAAAQ6AAAAgAemJ7AQ/original"/>
 <p>
 
-
-
 ## Quickstart
 
 ### 🤗 Hugging Face Transformers
@@ -85,9 +77,7 @@ 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/Ring-flash-2.0"
-
 model = AutoModelForCausalLM.from_pretrained(
     model_name,
     dtype="auto",
@@ -95,7 +85,6 @@ 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"},
@@ -107,7 +96,6 @@ 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=8192
@@ -115,7 +103,6 @@ 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]
 ```
 
@@ -146,25 +133,20 @@ pip install -e .
 ```python
 from transformers import AutoTokenizer
 from vllm import LLM, SamplingParams
-
 tokenizer = AutoTokenizer.from_pretrained("inclusionAI/Ring-flash-2.0")
-
 sampling_params = SamplingParams(temperature=0.7, top_p=0.8, repetition_penalty=1.05, max_tokens=16384)
-
 llm = LLM(model="inclusionAI/Ring-flash-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:
@@ -178,7 +160,9 @@ vllm serve inclusionAI/Ring-flash-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
 {
   ...,
@@ -189,24 +173,29 @@ 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
@@ -214,9 +203,10 @@ 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 \
@@ -224,6 +214,7 @@ 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.
 
@@ -237,8 +228,6 @@ curl -s http://localhost:${PORT}/v1/chat/completions \
 
 More usage can be found [here](https://docs.sglang.ai/basic_usage/send_request.html)
 
-
-
 ### Finetuning
 
 We recommend you to use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory) to [finetune Ring](https://github.com/inclusionAI/Ling-V2/blob/main/docs/llamafactory_finetuning.md).
@@ -246,5 +235,3 @@ We recommend you to use [Llama-Factory](https://github.com/hiyouga/LLaMA-Factory
 ## License
 
 This code repository is licensed under [the MIT License](https://github.com/inclusionAI/Ring-V2/blob/master/LICENSE).
-
-