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README.md

@@ -0,0 +1,262 @@
+# ERNIE-4.5-300B-A47B
+
+## Features
+Based on ERNIE-4.5-Base's text-specific parameters, we conduct post-training in two phases—supervised fine-tuning (SFT) and reinforcement learning (RL). Utilizing the unified rewarding system which accommodates both reasoning and general tasks, we employ a progressive RL training with the Unified Preference Optimization objective. We obtain superior performance across a broad spectrum of tasks, including creative writing, mathematical reasoning, and code generation. We summarize the features of our model as follows:
+
+* **Superior Performance in instruction following tasks**: Demonstrates exceptional capability to understand, prioritize, and execute complex user instructions and achieves top results on both internal human evaluation dataset and public benchmarks for instruction following.
+* **Factual Reliability & Knowledge Accuracy**: Excels on SimpleQA and CN-SimpleQA benchmarks, dramatically reducing hallucinations and ensuring responses are grounded in verifiable facts.
+* **Expressive**, **Persuasive** **responses**: Generates engaging, well-structured responses with personality, clear rationale, and persuasive argumentation — making it ideal for interactive and content-creation scenarios.
+* **Advanced Math & Coding Proficiency**: Delivers precise, step-by-step mathematical reasoning and generates clean, efficient code across multiple programming languages, supporting both algorithmic problem solving and real-world development. 
+
+
+
+## Model Overview
+ERNIE-4.5-300B-A47B is a text MoE Post-trained model, with 300B total parameters and 47B activated parameters for each token. The following are the model configuration details:
+
+|Key|Value|
+|-|-|
+|Modality|Text|
+|Training Stage|Pretraining|
+|Params(Total / Activated)|300B / 47B|
+|Layers|54|
+|Heads(Q/KV)|64 / 8|
+|Text Experts(Total / Activated)|64 / 8|
+|Vision Experts(Total / Activated)|64 / 8|
+|Context Length|131072|
+
+
+## Quickstart
+### Model Finetuning with ERNIEKit
+
+[ERNIEKit](https://github.com/PaddlePaddle/ERNIE) is a training toolkit based on PaddlePaddle, specifically designed for the ERNIE series of open-source large models. It provides comprehensive support for scenarios such as instruction fine-tuning (SFT, LoRA) and alignment training (DPO), ensuring optimal performance.
+
+Usage Examples:
+```bash
+# SFT
+erniekit train --stage SFT --model_name_or_path /baidu/ERNIE-4.5-300B-A47B --train_dataset_path your_dataset_path
+# DPO
+erniekit train --stage DPO --model_name_or_path /baidu/ERNIE-4.5-300B-A47B --train_dataset_path your_dataset_path
+```
+For more detailed examples, including SFT with LoRA, multi-GPU configurations, and advanced scripts, please refer to the examples folder within the [ERNIEKit](https://github.com/PaddlePaddle/ERNIE) repository.
+
+
+### Using `transformers` library
+
+The following contains a code snippet illustrating how to use the model generate content based on given inputs.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Baidu/ERNIE-4.5-300B-A47B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], add_special_tokens=False, return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    model_inputs.input_ids,
+    max_new_tokens=1024
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+
+# decode the generated ids
+generate_text = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print("generate_text:", generate_text)
+```
+
+### Using FastDeploy
+
+Service deployment can be quickly completed using FastDeploy in the following command. For more detailed usage instructions, please refer to the [FastDeploy Repository](https://github.com/PaddlePaddle/FastDeploy).
+
+**Note**: To deploy on a configuration with 4 GPUs each having at least 80G of memory, specify ```--quantization wint4```. If you specify ```--quantization wint8```, then resources for 8 GPUs are required.
+
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model BAIDU/ERNIE-4.5-300B-A47B-Paddle \
+       --port 8180 \
+       --metrics-port 8181 \
+       --quantization wint4 \
+       --tensor-parallel-size 8 \
+       --engine-worker-queue-port 8182 \
+       --max-model-len 32768 \ # Maximum supported number of tokens
+       --max-num-seqs 32 # Maximum concurrent processing capacity
+```
+
+To deploy the W4A8C8 quantized version using FastDeploy, you can run the following command.
+
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model BAIDU/ERNIE-4.5-300B-A47B-W4A8C8-TP4 \
+       --port 8180 \
+       --metrics-port 8181 \
+       --engine-worker-queue-port 8182 \
+       --tensor-parallel-size 4 \
+       --max-model-len 32768 \ # Maximum supported number of tokens
+       --max-num-seqs 32 # Maximum concurrent processing capacity
+```
+
+To deploy the WINT2 quantized version using FastDeploy on a single 141G GPU, you can run the following command.
+
+```bash
+python -m fastdeploy.entrypoints.openai.api_server \
+       --model "BAIDU/ERNIE-4.5-300B-A47B-2Bit" \
+       --port 8180 \
+       --metrics-port 8181 \
+       --engine-worker-queue-port 8182 \
+       --tensor-parallel-size 1 \
+       --max-model-len  32768 \ # Maximum supported number of tokens
+       --max-num-seqs 128 # Maximum concurrent processing capacity
+```
+
+The following contains a code snippet illustrating how to use ERNIE-4.5-300B-A47B-FP8 generate content based on given inputs.
+
+```bash
+from fastdeploy import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+]
+
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95, max_tokens=128)
+
+model = "BAIDU/ERNIE-4.5-300B-A47B-FP8"
+llm = LLM(model=model, tensor_parallel_size=8, max_model_len=8192, num_gpu_blocks_override=1024, engine_worker_queue_port=9981)
+
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs.text
+    print("generated_text", generated_text)
+```
+
+### Using vLLM
+vLLM is currently being adapted, priority can be given to using our fork repository [vllm](https://github.com/CSWYF3634076/vllm/tree/ernie)
+```bash
+# 80G * 16 GPU
+vllm serve Baidu/ERNIE-4.5-300B-A47B --trust-remote-code
+```
+```bash
+# FP8 online quantification 80G * 8 GPU
+vllm serve Baidu/ERNIE-4.5-300B-A47B --trust-remote-code --quantization fp8
+```
+
+
+
+## Best Practices
+### **Sampling Parameters**
+To achieve optimal performance, we suggest using `Temperature=0.8`, `TopP=0.8`.
+
+### Prompts for Web Search
+For Web Search, {references}, {date}, and {question} are arguments.
+
+For Chinese question, we use the prompt:
+
+```
+ernie_search_zh_prompt = \
+'''下面你会收到当前时间、多个不同来源的参考文章和一段对话。你的任务是阅读多个参考文章，并根据参考文章中的信息回答对话中的问题。
+以下是当前时间和参考文章：
+---------
+#当前时间
+{date}
+
+#参考文章
+{references}
+
+---------
+请注意：
+1. 回答必须结合问题需求和当前时间，对参考文章的可用性进行判断，避免在回答中使用错误或过时的信息。
+2. 当参考文章中的信息无法准确地回答问题时，你需要在回答中提供获取相应信息的建议，或承认无法提供相应信息。
+3. 你需要优先根据百科、官网、权威机构、专业网站等高权威性来源的信息来回答问题。
+4. 回复需要综合参考文章中的相关数字、案例、法律条文、公式等信息，使你的答案更专业。
+5. 当问题属于创作类任务时，需注意以下维度：
+   - 态度鲜明：观点、立场清晰明确，避免模棱两可，语言果断直接
+   - 文采飞扬：用词精准生动，善用修辞手法，增强感染力
+   - 有理有据：逻辑严密递进，结合权威数据/事实支撑论点
+---------
+下面请结合以上信息，回答问题，补全对话
+{question}'''
+```
+For English question, we use the prompt:
+
+```
+ernie_search_en_prompt = \
+'''
+Below you will be given the current time, multiple references from different sources, and a conversation. Your task is to read the references and use the information in them to answer the question in the conversation.
+Here are the current time and the references:
+---------
+#Current Time
+{date}
+
+#References
+{references}
+
+---------
+Please note:
+1. Based on the question’s requirements and the current time, assess the usefulness of the references to avoid using inaccurate or outdated information in the answer.  
+2. If the references do not provide enough information to accurately answer the question, you should suggest how to obtain the relevant information or acknowledge that you are unable to provide it.  
+3. Prioritize using information from highly authoritative sources such as encyclopedias, official websites, authoritative institutions, and professional websites when answering questions.
+4. Incorporate relevant numbers, cases, legal provisions, formulas, and other details from the references to make your answer more professional.
+5. For creative tasks, keep these dimensions in mind:
+   - Clear attitude: Clear views and positions, avoid ambiguity, and use decisive and direct language
+   - Brilliant writing: Precise and vivid words, good use of rhetoric, and enhance the appeal
+   - Well-reasoned: Rigorous logic and progressive, combined with authoritative data/facts to support the argument
+
+---------
+Now, using the information above, answer the question and complete the conversation:  
+{question}'''
+```
+
+
+Parameter notes:
+
+* {question} is the user’s question
+* {date} is the current time, and the recommended format is “YYYY-MM-DD HH:MM:SS, Day of the Week, Beijing/China.”
+* {references} is the references, and the recommended format is:
+
+```
+##参考文章1
+标题：周杰伦
+文章发布时间：2025-04-20
+内容：周杰伦(Jay Chou),1979年1月18日出生于台湾省新北市,祖籍福建省永春县,华语流行乐男歌手、音乐人、演员、导演、编剧,毕业于淡江中学。2000年,发行个人首张音乐专辑《Jay》。...
+来源网站网址：baike.baidu.com
+来源网站的网站名：百度百科
+
+##参考文章2
+...
+```
+
+
+
+## License
+
+The ERNIE 4.5 models are provided under the Apache License 2.0. This license permits commercial use, subject to its terms and conditions. Copyright (c) 2025 Baidu, Inc. All Rights Reserved.
+
+## Citation
+
+If you find ERNIE 4.5 useful or wish to use it in your projects, please kindly cite our technical report:
+
+```bibtex
+@misc{ernie2025technicalreport,
+      title={ERNIE 4.5 Technical Report},
+      author={Baidu ERNIE Team},
+      year={2025},
+      eprint={},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={}
+}
+```
+

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tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:34ef7db83df785924fb83d7b887b6e822a031c56e15cff40aaf9b982988180df
+size 1614363