Update README.md

README.md

@@ -21,7 +21,8 @@ base_model:
 Ring-lite is a lightweight, fully open-sourced MoE (Mixture of Experts) LLM designed for complex reasoning tasks. It is built upon the publicly available [Ling-lite-1.5](https://huggingface.co/inclusionAI/Ling-lite-1.5) model, which has 16.8B parameters with 2.75B activated parameters.. We use a joint training pipeline combining knowledge distillation with reinforcement learning, achieving performance comparable to state-of-the-art (SOTA) small-size reasoning models on challenging benchmarks (AIME, LiveCodeBench, and GPQA-Diamond) while activating only one-third of their parameters.
 
 
-
+## News
+[20250704] Ring-lite-0704: we update Ring-lite model, which supports two distinct reasoning modes: "**thinking on**" and "**thinking off**".
 ## Model Downloads
 
 <div align="center">
@@ -39,6 +40,20 @@ For a comprehensive evaluation of the quality of our reasoning models, we implem
     <img src="https://huggingface.co/inclusionAI/Ring-lite/resolve/main/performance.png" width="1000"/>
 <p>
 
+To compare the performance of Ring-lite-0704 and Ring-lite-0616, we evaluate the two models on a broader range of reasoning and general-purpose benchmarks, including instruction following, function calling, and creative writing. 
+| **Dataset** | **Ring-lite-0616** | **Ring-lite-0704** |
+| :---------: | :----------------: | :----------------: |
+| AIME 2024 | 76.6 | 79.0 |
+| AIME 2025 | 69.1 | 69.5 |
+| LiveCodeBench | 60.7 | 61.4 |
+| Codeforces (percentile) | 86.5 | 88.0 |
+| GPQA Diamond | 61.1 | 63.2 |
+| C-Eval | 59.0 | 65.4 |
+| MMLU-Pro | 60.0 | 63.0 |
+| ArenaHard | 27.8 | 62.7 |
+| IF-Eval | 51.6 | 54.3 |
+| BFCL_Live | 78.3 | 84.4 |
+| Creative Writing | 6.7 | 60.2 |
 
 
 More details are reported in our [technical report](https://arxiv.org/abs/2506.14731).
@@ -46,6 +61,12 @@ More details are reported in our [technical report](https://arxiv.org/abs/2506.1
 ## Quickstart
 
 ### 🤗 Hugging Face Transformers
+The newly updated **Ring-lite** model now supports two distinct reasoning modes: "**thinking on**" and "**thinking off**". These modes are controlled by the `enable_thinking` parameter in the `tokenizer.apply_chat_template()` function.
+* When `enable_thinking` is set to `True` (or omitted), the model operates in "**thinking on**" mode, where it generates and outputs the internal reasoning process.
+* When `enable_thinking` is explicitly set to `False`, the model runs in "**thinking off**" mode, skipping the reasoning step entirely and directly producing the final answer.
+This feature allows users to choose between detailed reasoning and concise output based on their specific needs.
+
+#### Thinking on
 Here is a code snippet to show you how to use the chat model with `transformers`:
 
 ```python
@@ -68,7 +89,45 @@ messages = [
 text = tokenizer.apply_chat_template(
     messages,
     tokenize=False,
-    add_generation_prompt=True
+    add_generation_prompt=True,
+    enable_thinking=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=8192
+)
+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]
+```
+
+#### Thinking off
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "inclusionAI/Ring-lite"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Give me a short introduction to large language models."
+messages = [
+    {"role": "system", "content": "You are Ring, an assistant created by inclusionAI"},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False
 )
 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)