init

README.md

@@ -1,3 +1,173 @@
----
-license: apache-2.0
----
+## MiniCPM-RR
+
+**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：
+- 出色的中文、英文重排序能力。
+- 出色的中英跨语言重排序能力。
+
+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。
+
+欢迎关注 RAG 套件系列：
+
+- 检索模型：[MiniCPM-R](https://huggingface.co/openbmb/MiniCPM-R)
+- 重排模型：[MiniCPM-RR](https://huggingface.co/openbmb/MiniCPM-RR)
+- 面向 RAG 场景的 LoRA 插件：[MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
+
+**MiniCPM-RR** is a bilingual & cross-lingual text re-ranking model developed by ModelBest Inc. and THUNLP, featuring:
+
+- Exceptional Chinese and English re-ranking capabilities.
+- Outstanding cross-lingual re-ranking capabilities between Chinese and English.
+
+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.
+
+We also invite you to explore the RAG toolkit series:
+
+- Retrieval Model: [MiniCPM-R](https://huggingface.co/openbmb/MiniCPM-R)
+- Re-ranking Model: [MiniCPM-RR](https://huggingface.co/openbmb/MiniCPM-RR)
+- LoRA Plugin for RAG scenarios: [MiniCPM3-RAG-LoRA](https://huggingface.co/openbmb/MiniCPM3-RAG-LoRA)
+
+## 模型信息 Model Information
+
+- 模型大小：2.4B
+- 最大输入token数：1024
+
+- Model Size: 2.4B
+- Max Input Tokens: 1024
+
+## 使用方法 Usage
+
+### 输入格式 Input Format
+
+本模型支持指令，输入格式如下：
+
+MiniCPM-RR supports instructions in the following format:
+
+```
+<s>Instruction: {{ instruction }} Query: {{ query }}</s>{{ document }}
+```
+
+例如：
+
+For example:
+
+```
+<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）
+```
+
+```
+<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)
+```
+
+也可以不提供指令，即采取如下格式：
+
+MiniCPM-RR also works in instruction-free mode in the following format:
+
+```
+<s>Query: {{ query }}</s>{{ document }}
+```
+
+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。
+
+When running evaluation on BEIR and C-MTEB/Retrieval, we use instructions in `instructions.json`. For other evaluations, we do not use instructions. 
+
+### 环境要求 Requirements
+
+```
+transformers==4.37.2
+flash-attn>2.3.5
+```
+
+### 示例脚本 Demo
+
+```python
+from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import numpy as np
+
+model_name = "openbmb/MiniCPM-RR"
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+tokenizer.padding_side = "right"
+model = AutoModelForSequenceClassification.from_pretrained(model_name, trust_remote_code=True,attn_implementation="flash_attention_2", torch_dtype=torch.float16).to("cuda")
+model.eval()
+max_len_q, max_len_d = 512, 512
+
+def tokenize_our(query,doc):
+    input_id_query = tokenizer.encode(query, add_special_tokens=False, max_length=max_len_q, truncation=True)
+    input_id_doc = tokenizer.encode(doc, add_special_tokens=False, max_length=max_len_d, truncation=True)
+    pad_input = {"input_ids": [tokenizer.bos_token_id] + input_id_query + [tokenizer.eos_token_id] + input_id_doc}
+    return tokenizer.pad(
+        pad_input,
+        padding="max_length",
+        max_length=max_len_q + max_len_d + 2,
+        return_tensors="pt",
+    )
+
+@torch.no_grad()
+def rerank(input_query, input_docs):
+    tokenized_inputs = [tokenize_our(input_query, input_doc).to("cuda") for input_doc in input_docs]
+    input_ids = {
+      "input_ids": [tokenized_input["input_ids"] for tokenized_input in tokenized_inputs],
+      "attention_mask": [tokenized_input["attention_mask"] for tokenized_input in tokenized_inputs]
+    }
+
+    for k in input_ids:
+      input_ids[k] = torch.stack(input_ids[k]).to("cuda")
+    outputs = model(**input_ids)
+    score = outputs.logits
+    return score.float().detach().cpu().numpy()
+
+queries = ["中国的首都是哪里？"]
+passages = [["beijing", "shanghai"]]
+
+INSTRUCTION = "Query: "
+queries = [INSTRUCTION + query for query in queries]
+
+scores = []
+for i in range(len(queries)):
+    print(queries[i])
+    scores.append(rerank(queries[i],passages[i]))
+
+print(np.array(scores))  # [[[-4.7421875][-8.8515625]]]
+```
+
+## 实验结果 Evaluation Results
+
+### 中文���英文重排序结果 CN/EN Re-ranking Results
+
+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。
+
+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.
+
+
+| 模型 Model            | C-MTEB/Retrieval (NDCG@10) | BEIR (NDCG@10) |
+|----------------------------|-------------------|---------------|
+| bge-large-zh-v1.5（Retriever for Chinese）  | 70.46             | -             |
+| bge-large-en-v1.5（Retriever for English）  | -                 | 54.29         |
+| bge-reranker-v2-m3         | 71.82             | 55.36         |
+| bge-reranker-v2-minicpm-28 | 73.51             | 59.86         |
+| bge-reranker-v2-gemma      | 71.74             | 60.71         |
+| bge-reranker-v2.5-gemma2   | -                 | **63.67**     |
+| MiniCPM-RR                 | **76.79**         | 61.32        |
+
+### 中英跨语言重排序结果 CN-EN Cross-lingual Re-ranking Results
+
+对bge-m3(Dense)检索的top100进行重排。
+
+We re-rank top-100 documents from `bge-m3` (Dense).
+
+| 模型 Model                      | MKQA EN-CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
+|------------------------------------|--------------------|--------------------|--------------------|
+| bge-m3 (Dense)（Retriever）              | 66.4               | 30.49              | 41.09              |
+| jina-reranker-v2-base-multilingual | 69.33              | 36.66              | 50.03              |
+| bge-reranker-v2-m3                 | 69.75              | 40.98              | 49.67              |
+| gte-multilingual-reranker-base     | 68.51              | 38.74              | 45.3              |
+| MiniCPM-RR                         | **71.73**          | **43.65**          | **50.59**          |
+
+## 许可证 License
+
+- 本仓库中代码依照 [Apache-2.0 协议](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE)开源。
+- 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)。
+- 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)。
+
+* The code in this repo is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. 
+* 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).
+* 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

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+{
+  "_name_or_path": "openbmb/MiniCPM-RR",
+  "architectures": [
+      "MiniCPM"
+  ],
+  "auto_map": {
+      "AutoConfig": "configuration_minicpm.MiniCPMConfig",
+      "AutoModel": "modeling_minicpm.MiniCPMModel",
+      "AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
+      "AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
+      "AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 2304,
+  "initializer_range": 0.1,
+  "intermediate_size": 5760,
+  "is_causal": false,
+  "max_position_embeddings": 2048,
+  "num_attention_heads": 36,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 36,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0",
+  "use_cache": true,
+  "vocab_size": 122753,
+  "scale_emb": 12,
+  "dim_model_base": 256,
+  "scale_depth": 1.4,
+  "num_labels": 1
+}

configuration_minicpm.py

@@ -0,0 +1,204 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" MiniCPM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class MiniCPMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MiniCPM-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniCPMModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
+            MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MiniCPMModel, MiniCPMConfig
+
+    >>> # Initializing a MiniCPM minicpm-7b style configuration
+    >>> configuration = MiniCPMConfig()
+
+    >>> # Initializing a model from the minicpm-7b style configuration
+    >>> model = MiniCPMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minicpm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        scale_emb=1,
+        dim_model_base=1,
+        scale_depth=1,
+        is_causal=True,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.scale_emb = scale_emb
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+        self.is_causal = is_causal
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        try:
+            import flash_attn
+            self._attn_implementation = "flash_attention_2"
+        except:
+            pass
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

instruction.json

@@ -0,0 +1,24 @@
+{
+ "fiqa": "Instruction: Given a financial question, retrieve user replies that best answer the question. Query: ", 
+ "dbpedia": "Instruction: Given a query, retrieve relevant entity descriptions from DBPedia. Query: ", 
+ "CmedqaRetrieval": "Instruction: 为这个医疗问题检索相关回答。 Query: ", 
+ "nfcorpus": "Instruction: Given a question, retrieve relevant documents that best answer the question. Query: ", 
+ "touche2020": "Instruction: Given a question, retrieve detailed and persuasive arguments that answer the question. Query: ", 
+ "CovidRetrieval": "Instruction: 为这个问题检索相关政策回答。 Query: ", 
+ "scifact": "Instruction: Given a scientific claim, retrieve documents that support or refute the claim. Query: ", 
+ "scidocs": "Instruction: Given a scientific paper title, retrieve paper abstracts that are cited by the given paper. Query: ", 
+ "nq": "Instruction: Given a question, retrieve Wikipedia passages that answer the question. Query: ", 
+ "T2Retrieval": "Instruction: 为这个问题检索相关段落。 Query: ", 
+ "VideoRetrieval": "Instruction: 为这个电影标题检索相关段落。 Query: ",
+ "DuRetrieval": "Instruction: 为这个问题检索相关百度知道回答。 Query: ", 
+ "MMarcoRetrieval": "Instruction: 为这个查询检索相关段落。 Query: ", 
+ "hotpotqa": "Instruction: Given a multi-hop question, retrieve documents that can help answer the question. Query: ",
+ "quora": "Instruction: Given a question, retrieve questions that are semantically equivalent to the given question. Query: ",
+ "climate-fever": "Instruction: Given a claim about climate change, retrieve documents that support or refute the claim. Query: ",
+ "arguana": "Instruction: Given a claim, find documents that refute the claim. Query: ", 
+ "fever": "Instruction: Given a claim, retrieve documents that support or refute the claim. Query: ", 
+ "trec-covid": "Instruction: Given a query on COVID-19, retrieve documents that answer the query. Query: ", 
+ "msmarco": "Instruction: Given a web search query, retrieve relevant passages that answer the query. Query: ", 
+ "EcomRetrieval": "Instruction: 为这个查询检索相关商品标题。 Query: ", 
+ "MedicalRetrieval": "Instruction: 为这个医学问题检索相关回答。 Query: "
+ }

model-00001-of-00002.safetensors

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model.safetensors.index.json

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