初始化项目，由ModelHub XC社区提供模型

Model: inclusionAI/Ring-lite-distill-preview
Source: Original Platform

README.md

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+---
+license: mit
+language:
+- zh
+- en
+base_model:
+- inclusionAI/Ling-lite
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+# Ring-lite-distill-preview
+
+<p align="center">
+    <img src="https://huggingface.co/inclusionAI/Ring-lite-distill-preview/resolve/main/ant-bailing.png" width="100"/>
+<p>
+
+<p align="center">
+          🤗 <a href="https://huggingface.co/inclusionAI">Hugging Face</a>
+<p>
+
+## Introduction
+
+Ring-lite-distill-preview is an MoE LLM provided and open-sourced by InclusionAI, which has 16.8B parameters with 2.75B activated parameters. It was fine-tuned from [Ling-lite](https://modelscope.cn/models/inclusionAI/Ling-lite) using extensive reasoning-focused instruction data. This model delivers performance comparable to DeepSeek-R1-Distill-Qwen-7B on reasoning benchmarks while achieving better results on general benchmarks, especially superior performance on function-calling evaluation benchmarks (e.g., TEval, BFCl_v2) and instruction-following benchmarks (e.g., IFEval). This demonstrates that Ring-lite-distill is a more balanced and versatile model. Additionaly, it maintains competitive latency and throughput compared to other reasoning LLMs of similar size.
+
+## Model Downloads
+
+<div align="center">
+
+|     **Model**      | **#Total Params** | **#Activated Params** | **Context Length** | **Download** |
+| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
+| Ring-lite-distill-preview |       16.8B       |         2.75B         |        64K         |      [🤗 HuggingFace](https://huggingface.co/inclusionAI/Ring-lite-distill) |
+
+</div>
+
+## Evaluation
+In order to fully evaluate the model's performance, we examined Ring-lite-distill-preview in terms of both reasoning ability and general ability.
+### Reasoning ability
+
+<div align="center">
+
+|     **Model**      | **AIME24** | **MATH-500** | **GPQA-diamond** | **LiveCodeBench** |
+| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
+| DeepSeek-R1-Distill-Qwen-7B (reported) |       55.5       |         92.8         |        49.1           |          37.6       |
+| DeepSeek-R1-Distill-Qwen-7B (reproduce)  |       53.2       |         93.7         |        50.4         |         36.5       |
+| Ring-lite-distill-preview |       56.3       |         93.7         |        46.2        |        31.9       |
+
+</div>
+
+### General ability
+
+<div align="center">
+
+|     **Model**      | **IFEval**  | **T-eval** | **BFCL_v2** | **MMLU** |
+| :----------------: | :---------------: | :-------------------: | :----------------: | :----------: |
+| DeepSeek-R1-Distill-Qwen-7B (reproduce)  |       39.3       |         26.9          | 38.9 | 44.1 |
+| Ring-lite-distill-preview |      75.3 | 81.3 | 63.0 | 63.3 |
+
+</div>
+
+More details will be reported in our [technical report](https://github.com/inclusionAI/Ring/blob/main/Ring_Lite_Distill_Preview.pdf).
+
+## Quickstart
+
+### 🤗 Hugging Face Transformers
+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-lite-distill-preview"
+
+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
+)
+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]
+```
+
+## Dataset
+The training data of Ring-lite-distill-preview will be released soon. 
+
+## Deployment
+Please refer to [GitHub](https://github.com/inclusionAI/Ring/blob/main/README.md)
+
+## License
+This code repository is licensed under [the MIT License](https://huggingface.co/inclusionAI/Ring-lite-distill/blob/main/LICENSE).
+
+## Citation
+[TBD]