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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## MiniCPM-RR
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+
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+ **MiniCPM-RR** 是面壁智能与清华大学自然语言处理实验室(THUNLP)共同开发的中英双语言文本重排序模型,有如下特点:
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+ - 出色的中文、英文重排序能力。
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+ - 出色的中英跨语言重排序能力。
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+
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+ MiniCPM-RR 基于 [MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) 训练,结构上采取双向注意力。采取多阶段训练方式,共使用包括开源数据、机造数据、闭源数据在内的约 600 万条训练数据。
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+
9
+ 欢迎关注 RAG 套件系列:
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+
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+ - 检索模型:[MiniCPM-R](https://huggingface.co/openbmb/MiniCPM-R)
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+ - 重排模型:[MiniCPM-RR](https://huggingface.co/openbmb/MiniCPM-RR)
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+ - 面向 RAG 场景的 LoRA 插件:[MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
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+
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+ **MiniCPM-RR** is a bilingual & cross-lingual text re-ranking model developed by ModelBest Inc. and THUNLP, featuring:
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+
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+ - Exceptional Chinese and English re-ranking capabilities.
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+ - Outstanding cross-lingual re-ranking capabilities between Chinese and English.
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+
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+ MiniCPM-RR is trained based on [MiniCPM-2B-sft-bf16](https://huggingface.co/openbmb/MiniCPM-2B-sft-bf16) and incorporates bidirectional attention in its architecture. The model underwent multi-stage training using approximately 6 million training examples, including open-source, synthetic, and proprietary data.
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+
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+ We also invite you to explore the RAG toolkit series:
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+
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+ - Retrieval Model: [MiniCPM-R](https://huggingface.co/openbmb/MiniCPM-R)
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+ - Re-ranking Model: [MiniCPM-RR](https://huggingface.co/openbmb/MiniCPM-RR)
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+ - LoRA Plugin for RAG scenarios: [MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
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+
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+ ## 模型信息 Model Information
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+
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+ - 模型大小:2.4B
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+ - 最大输入token数:1024
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+
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+ - Model Size: 2.4B
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+ - Max Input Tokens: 1024
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+
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+ ## 使用方法 Usage
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+
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+ ### 输入格式 Input Format
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+
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+ 本模型支持指令,输入格式如下:
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+
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+ MiniCPM-RR supports instructions in the following format:
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+
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+ ```
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+ <s>Instruction: {{ instruction }} Query: {{ query }}</s>{{ document }}
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+ ```
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+
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+ 例如:
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+
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+ For example:
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+
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+ ```
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+ <s>Instruction: 为这个医学问题检索相关回答。Query: 咽喉癌的成因是什么?</s>(文档省略)
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+ ```
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+
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+ ```
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+ <s>Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: However the warming trend is slower than most climate models have forecast.</s>(document omitted)
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+ ```
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+
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+ 也可以不提供指令,即采取如下格式:
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+
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+ MiniCPM-RR also works in instruction-free mode in the following format:
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+
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+ ```
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+ <s>Query: {{ query }}</s>{{ document }}
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+ ```
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+
68
+ 我们在BEIR与C-MTEB/Retrieval上测试时使用的指令见 `instructions.json`,其他测试不使用指令。
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+
70
+ When running evaluation on BEIR and C-MTEB/Retrieval, we use instructions in `instructions.json`. For other evaluations, we do not use instructions.
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+
72
+ ### 环境要求 Requirements
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+
74
+ ```
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+ transformers==4.37.2
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+ flash-attn>2.3.5
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+ ```
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+
79
+ ### 示例脚本 Demo
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+
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+ ```python
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+ from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
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+ import torch
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+ import numpy as np
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+
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+ model_name = "openbmb/MiniCPM-RR"
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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+ tokenizer.padding_side = "right"
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+ model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
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+ model.eval()
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+ max_len_q, max_len_d = 512, 512
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+
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+ def tokenize_our(query,doc):
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+ input_id_query = tokenizer.encode(query, add_special_tokens=False, max_length=max_len_q, truncation=True)
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+ input_id_doc = tokenizer.encode(doc, add_special_tokens=False, max_length=max_len_d, truncation=True)
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+ pad_input = {"input_ids": [tokenizer.bos_token_id] + input_id_query + [tokenizer.eos_token_id] + input_id_doc}
97
+ return tokenizer.pad(
98
+ pad_input,
99
+ padding="max_length",
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+ max_length=max_len_q + max_len_d + 2,
101
+ return_tensors="pt",
102
+ )
103
+
104
+ @torch.no_grad()
105
+ def rerank(input_query, input_docs):
106
+ tokenized_inputs = [tokenize_our(input_query, input_doc).to("cuda") for input_doc in input_docs]
107
+ input_ids = {
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+ "input_ids": [tokenized_input["input_ids"] for tokenized_input in tokenized_inputs],
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+ "attention_mask": [tokenized_input["attention_mask"] for tokenized_input in tokenized_inputs]
110
+ }
111
+
112
+ for k in input_ids:
113
+ input_ids[k] = torch.stack(input_ids[k]).to("cuda")
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+ outputs = model(**input_ids)
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+ score = outputs.logits
116
+ return score.float().detach().cpu().numpy()
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+
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+ queries = ["中国的首都是哪里?"]
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+ passages = [["beijing", "shanghai"]]
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+
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+ INSTRUCTION = "Query: "
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+ queries = [INSTRUCTION + query for query in queries]
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+
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+ scores = []
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+ for i in range(len(queries)):
126
+ print(queries[i])
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+ scores.append(rerank(queries[i],passages[i]))
128
+
129
+ print(np.array(scores)) # [[[-4.7421875][-8.8515625]]]
130
+ ```
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+
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+ ## 实验结果 Evaluation Results
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+
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+ ### 中文���英文重排序结果 CN/EN Re-ranking Results
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+
136
+ 中文对`bge-large-zh-v1.5`检索的top-100进行重排,英文对`bge-large-en-v1.5`检索的top-100进行重排。
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+
138
+ We re-rank top-100 docments from `bge-large-zh-v1.5` in C-MTEB/Retrieval and from `bge-large-en-v1.5` in BEIR.
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+
140
+
141
+ | 模型 Model | C-MTEB/Retrieval (NDCG@10) | BEIR (NDCG@10) |
142
+ |----------------------------|-------------------|---------------|
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+ | bge-large-zh-v1.5(Retriever for Chinese) | 70.46 | - |
144
+ | bge-large-en-v1.5(Retriever for English) | - | 54.29 |
145
+ | bge-reranker-v2-m3 | 71.82 | 55.36 |
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+ | bge-reranker-v2-minicpm-28 | 73.51 | 59.86 |
147
+ | bge-reranker-v2-gemma | 71.74 | 60.71 |
148
+ | bge-reranker-v2.5-gemma2 | - | **63.67** |
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+ | MiniCPM-RR | **76.79** | 61.32 |
150
+
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+ ### 中英跨语言重排序结果 CN-EN Cross-lingual Re-ranking Results
152
+
153
+ 对bge-m3(Dense)检索的top100进行重排。
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+
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+ We re-rank top-100 documents from `bge-m3` (Dense).
156
+
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+ | 模型 Model | MKQA EN-CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
158
+ |------------------------------------|--------------------|--------------------|--------------------|
159
+ | bge-m3 (Dense)(Retriever) | 66.4 | 30.49 | 41.09 |
160
+ | jina-reranker-v2-base-multilingual | 69.33 | 36.66 | 50.03 |
161
+ | bge-reranker-v2-m3 | 69.75 | 40.98 | 49.67 |
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+ | gte-multilingual-reranker-base | 68.51 | 38.74 | 45.3 |
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+ | MiniCPM-RR | **71.73** | **43.65** | **50.59** |
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+
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+ ## 许可证 License
166
+
167
+ - 本仓库中代码依照 [Apache-2.0 协议](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE)开源。
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+ - MiniCPM-RR 模型权重的使用则需要遵循 [MiniCPM 模型协议](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md)。
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+ - MiniCPM-RR 模型权重对学术研究完全开放。如需将模型用于商业用途,请填写[此问卷](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g)。
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+
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+ * The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
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+ * The usage of MiniCPM-RR model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
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+ * The models and weights of MiniCPM-RR are completely free for academic research. After filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, MiniCPM-RR weights are also available for free commercial use.
config.json ADDED
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+ {
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+ "_name_or_path": "openbmb/MiniCPM-RR",
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+ "architectures": [
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+ "MiniCPM"
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+ ],
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+ "auto_map": {
7
+ "AutoConfig": "configuration_minicpm.MiniCPMConfig",
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+ "AutoModel": "modeling_minicpm.MiniCPMModel",
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+ "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
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+ "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
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+ "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
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+ },
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 2304,
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+ "initializer_range": 0.1,
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+ "intermediate_size": 5760,
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+ "is_causal": false,
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+ "max_position_embeddings": 2048,
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+ "num_attention_heads": 36,
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+ "num_hidden_layers": 40,
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+ "num_key_value_heads": 36,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.36.0",
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+ "use_cache": true,
29
+ "vocab_size": 122753,
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+ "scale_emb": 12,
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+ "dim_model_base": 256,
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+ "scale_depth": 1.4,
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+ "num_labels": 1
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+ }
configuration_minicpm.py ADDED
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+ # coding=utf-8
2
+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
3
+ #
4
+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
5
+ # and OPT implementations in this library. It has been modified from its
6
+ # original forms to accommodate minor architectural differences compared
7
+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
8
+ #
9
+ # Licensed under the Apache License, Version 2.0 (the "License");
10
+ # you may not use this file except in compliance with the License.
11
+ # You may obtain a copy of the License at
12
+ #
13
+ # http://www.apache.org/licenses/LICENSE-2.0
14
+ #
15
+ # Unless required by applicable law or agreed to in writing, software
16
+ # distributed under the License is distributed on an "AS IS" BASIS,
17
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
18
+ # See the License for the specific language governing permissions and
19
+ # limitations under the License.
20
+ """ MiniCPM model configuration"""
21
+
22
+ from transformers.configuration_utils import PretrainedConfig
23
+ from transformers.utils import logging
24
+
25
+
26
+ logger = logging.get_logger(__name__)
27
+
28
+ MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
29
+
30
+
31
+ class MiniCPMConfig(PretrainedConfig):
32
+ r"""
33
+ This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
34
+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
35
+ defaults will yield a similar configuration to that of the MiniCPM-7B.
36
+
37
+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
38
+ documentation from [`PretrainedConfig`] for more information.
39
+
40
+
41
+ Args:
42
+ vocab_size (`int`, *optional*, defaults to 32000):
43
+ Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
44
+ `inputs_ids` passed when calling [`MiniCPMModel`]
45
+ hidden_size (`int`, *optional*, defaults to 4096):
46
+ Dimension of the hidden representations.
47
+ intermediate_size (`int`, *optional*, defaults to 11008):
48
+ Dimension of the MLP representations.
49
+ num_hidden_layers (`int`, *optional*, defaults to 32):
50
+ Number of hidden layers in the Transformer decoder.
51
+ num_attention_heads (`int`, *optional*, defaults to 32):
52
+ Number of attention heads for each attention layer in the Transformer decoder.
53
+ num_key_value_heads (`int`, *optional*):
54
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
55
+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
56
+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
57
+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
58
+ by meanpooling all the original heads within that group. For more details checkout [this
59
+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
60
+ `num_attention_heads`.
61
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
62
+ The non-linear activation function (function or string) in the decoder.
63
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
64
+ The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
65
+ MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
66
+ initializer_range (`float`, *optional*, defaults to 0.02):
67
+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
68
+ rms_norm_eps (`float`, *optional*, defaults to 1e-06):
69
+ The epsilon used by the rms normalization layers.
70
+ use_cache (`bool`, *optional*, defaults to `True`):
71
+ Whether or not the model should return the last key/values attentions (not used by all models). Only
72
+ relevant if `config.is_decoder=True`.
73
+ pad_token_id (`int`, *optional*):
74
+ Padding token id.
75
+ bos_token_id (`int`, *optional*, defaults to 1):
76
+ Beginning of stream token id.
77
+ eos_token_id (`int`, *optional*, defaults to 2):
78
+ End of stream token id.
79
+ pretraining_tp (`int`, *optional*, defaults to 1):
80
+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
81
+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
82
+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
83
+ issue](https://github.com/pytorch/pytorch/issues/76232).
84
+ tie_word_embeddings (`bool`, *optional*, defaults to `False`):
85
+ Whether to tie weight embeddings
86
+ rope_theta (`float`, *optional*, defaults to 10000.0):
87
+ The base period of the RoPE embeddings.
88
+ rope_scaling (`Dict`, *optional*):
89
+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
90
+ strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
91
+ `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
92
+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
93
+ these scaling strategies behave:
94
+ https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
95
+ experimental feature, subject to breaking API changes in future versions.
96
+ attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
97
+ Whether to use a bias in the query, key, value and output projection layers during self-attention.
98
+ attention_dropout (`float`, *optional*, defaults to 0.0):
99
+ The dropout ratio for the attention probabilities.
100
+
101
+ ```python
102
+ >>> from transformers import MiniCPMModel, MiniCPMConfig
103
+
104
+ >>> # Initializing a MiniCPM minicpm-7b style configuration
105
+ >>> configuration = MiniCPMConfig()
106
+
107
+ >>> # Initializing a model from the minicpm-7b style configuration
108
+ >>> model = MiniCPMModel(configuration)
109
+
110
+ >>> # Accessing the model configuration
111
+ >>> configuration = model.config
112
+ ```"""
113
+
114
+ model_type = "minicpm"
115
+ keys_to_ignore_at_inference = ["past_key_values"]
116
+
117
+ def __init__(
118
+ self,
119
+ vocab_size=32000,
120
+ hidden_size=4096,
121
+ intermediate_size=11008,
122
+ num_hidden_layers=32,
123
+ num_attention_heads=32,
124
+ num_key_value_heads=None,
125
+ hidden_act="silu",
126
+ max_position_embeddings=2048,
127
+ initializer_range=0.02,
128
+ rms_norm_eps=1e-6,
129
+ use_cache=True,
130
+ pad_token_id=None,
131
+ bos_token_id=1,
132
+ eos_token_id=2,
133
+ pretraining_tp=1,
134
+ tie_word_embeddings=True,
135
+ rope_theta=10000.0,
136
+ rope_scaling=None,
137
+ attention_bias=False,
138
+ attention_dropout=0.0,
139
+ scale_emb=1,
140
+ dim_model_base=1,
141
+ scale_depth=1,
142
+ is_causal=True,
143
+ **kwargs,
144
+ ):
145
+ self.vocab_size = vocab_size
146
+ self.max_position_embeddings = max_position_embeddings
147
+ self.hidden_size = hidden_size
148
+ self.intermediate_size = intermediate_size
149
+ self.num_hidden_layers = num_hidden_layers
150
+ self.num_attention_heads = num_attention_heads
151
+
152
+ # for backward compatibility
153
+ if num_key_value_heads is None:
154
+ num_key_value_heads = num_attention_heads
155
+
156
+ self.num_key_value_heads = num_key_value_heads
157
+ self.hidden_act = hidden_act
158
+ self.initializer_range = initializer_range
159
+ self.rms_norm_eps = rms_norm_eps
160
+ self.pretraining_tp = pretraining_tp
161
+ self.use_cache = use_cache
162
+ self.rope_theta = rope_theta
163
+ self.rope_scaling = rope_scaling
164
+ self._rope_scaling_validation()
165
+ self.attention_bias = attention_bias
166
+ self.attention_dropout = attention_dropout
167
+ self.scale_emb = scale_emb
168
+ self.dim_model_base = dim_model_base
169
+ self.scale_depth = scale_depth
170
+ self.is_causal = is_causal
171
+
172
+ super().__init__(
173
+ pad_token_id=pad_token_id,
174
+ bos_token_id=bos_token_id,
175
+ eos_token_id=eos_token_id,
176
+ tie_word_embeddings=tie_word_embeddings,
177
+ **kwargs,
178
+ )
179
+ try:
180
+ import flash_attn
181
+ self._attn_implementation = "flash_attention_2"
182
+ except:
183
+ pass
184
+
185
+ def _rope_scaling_validation(self):
186
+ """
187
+ Validate the `rope_scaling` configuration.
188
+ """
189
+ if self.rope_scaling is None:
190
+ return
191
+
192
+ if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
193
+ raise ValueError(
194
+ "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
195
+ f"got {self.rope_scaling}"
196
+ )
197
+ rope_scaling_type = self.rope_scaling.get("type", None)
198
+ rope_scaling_factor = self.rope_scaling.get("factor", None)
199
+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
200
+ raise ValueError(
201
+ f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
202
+ )
203
+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
204
+ raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
instruction.json ADDED
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1
+ {
2
+ "fiqa": "Instruction: Given a financial question, retrieve user replies that best answer the question. Query: ",
3
+ "dbpedia": "Instruction: Given a query, retrieve relevant entity descriptions from DBPedia. Query: ",
4
+ "CmedqaRetrieval": "Instruction: 为这个医疗问题检索相关回答。 Query: ",
5
+ "nfcorpus": "Instruction: Given a question, retrieve relevant documents that best answer the question. Query: ",
6
+ "touche2020": "Instruction: Given a question, retrieve detailed and persuasive arguments that answer the question. Query: ",
7
+ "CovidRetrieval": "Instruction: 为这个问题检索相关政策回答。 Query: ",
8
+ "scifact": "Instruction: Given a scientific claim, retrieve documents that support or refute the claim. Query: ",
9
+ "scidocs": "Instruction: Given a scientific paper title, retrieve paper abstracts that are cited by the given paper. Query: ",
10
+ "nq": "Instruction: Given a question, retrieve Wikipedia passages that answer the question. Query: ",
11
+ "T2Retrieval": "Instruction: 为这个问题检索相关段落。 Query: ",
12
+ "VideoRetrieval": "Instruction: 为这个电影标题检索相关段落。 Query: ",
13
+ "DuRetrieval": "Instruction: 为这个问题检索相关百度知道回答。 Query: ",
14
+ "MMarcoRetrieval": "Instruction: 为这个查询检索相关段落。 Query: ",
15
+ "hotpotqa": "Instruction: Given a multi-hop question, retrieve documents that can help answer the question. Query: ",
16
+ "quora": "Instruction: Given a question, retrieve questions that are semantically equivalent to the given question. Query: ",
17
+ "climate-fever": "Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: ",
18
+ "arguana": "Instruction: Given a claim, find documents that refute the claim. Query: ",
19
+ "fever": "Instruction: Given a claim, retrieve documents that support or refute the claim. Query: ",
20
+ "trec-covid": "Instruction: Given a query on COVID-19, retrieve documents that answer the query. Query: ",
21
+ "msmarco": "Instruction: Given a web search query, retrieve relevant passages that answer the query. Query: ",
22
+ "EcomRetrieval": "Instruction: 为这个查询检索相关商品标题。 Query: ",
23
+ "MedicalRetrieval": "Instruction: 为这个医学问题检索相关回答。 Query: "
24
+ }
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modeling_minicpm.py ADDED
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