Kaguya-19
commited on
Commit
·
7243e76
1
Parent(s):
36a173e
init
Browse files- README.md +173 -3
- config.json +34 -0
- configuration_minicpm.py +204 -0
- instruction.json +24 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +370 -0
- modeling_minicpm.py +1453 -0
- special_tokens_map.json +30 -0
- tokenizer.model +3 -0
- tokenizer_config.json +41 -0
README.md
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## MiniCPM-RR
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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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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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欢迎关注 RAG 套件系列:
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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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**MiniCPM-RR** is a bilingual & cross-lingual text re-ranking model developed by ModelBest Inc. and THUNLP, featuring:
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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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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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We also invite you to explore the RAG toolkit series:
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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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## 模型信息 Model Information
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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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- Model Size: 2.4B
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- Max Input Tokens: 1024
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## 使用方法 Usage
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### 输入格式 Input Format
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本模型支持指令,输入格式如下:
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MiniCPM-RR supports instructions in the following format:
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```
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<s>Instruction: {{ instruction }} Query: {{ query }}</s>{{ document }}
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```
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例如:
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For example:
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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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<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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也可以不提供指令,即采取如下格式:
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MiniCPM-RR also works in instruction-free mode in the following format:
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```
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<s>Query: {{ query }}</s>{{ document }}
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```
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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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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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### 环境要求 Requirements
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```
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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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### 示例脚本 Demo
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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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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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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}
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return tokenizer.pad(
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pad_input,
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padding="max_length",
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max_length=max_len_q + max_len_d + 2,
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return_tensors="pt",
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)
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@torch.no_grad()
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def rerank(input_query, input_docs):
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tokenized_inputs = [tokenize_our(input_query, input_doc).to("cuda") for input_doc in input_docs]
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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]
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}
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for k in input_ids:
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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
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return score.float().detach().cpu().numpy()
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queries = ["中国的首都是哪里?"]
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passages = [["beijing", "shanghai"]]
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INSTRUCTION = "Query: "
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queries = [INSTRUCTION + query for query in queries]
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scores = []
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for i in range(len(queries)):
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print(queries[i])
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scores.append(rerank(queries[i],passages[i]))
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print(np.array(scores)) # [[[-4.7421875][-8.8515625]]]
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```
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## 实验结果 Evaluation Results
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### 中文���英文重排序结果 CN/EN Re-ranking Results
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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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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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| 模型 Model | C-MTEB/Retrieval (NDCG@10) | BEIR (NDCG@10) |
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|----------------------------|-------------------|---------------|
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| bge-large-zh-v1.5(Retriever for Chinese) | 70.46 | - |
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| bge-large-en-v1.5(Retriever for English) | - | 54.29 |
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| bge-reranker-v2-m3 | 71.82 | 55.36 |
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| bge-reranker-v2-minicpm-28 | 73.51 | 59.86 |
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| bge-reranker-v2-gemma | 71.74 | 60.71 |
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| bge-reranker-v2.5-gemma2 | - | **63.67** |
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| MiniCPM-RR | **76.79** | 61.32 |
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### 中英跨语言重排序结果 CN-EN Cross-lingual Re-ranking Results
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对bge-m3(Dense)检索的top100进行重排。
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We re-rank top-100 documents from `bge-m3` (Dense).
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| 模型 Model | MKQA EN-CN (Recall@20) | NeuCLIR22 (NDCG@10) | NeuCLIR23 (NDCG@10) |
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|------------------------------------|--------------------|--------------------|--------------------|
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| bge-m3 (Dense)(Retriever) | 66.4 | 30.49 | 41.09 |
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| jina-reranker-v2-base-multilingual | 69.33 | 36.66 | 50.03 |
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| 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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## 许可证 License
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- 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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* 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.
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config.json
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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": {
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"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,
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"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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}
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configuration_minicpm.py
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# coding=utf-8
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# 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
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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 |
+
}
|
model-00001-of-00002.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:652b0bf6777d4b03b63935abce72739d716a115c025ed15a810b8f61e077465b
|
3 |
+
size 4993234616
|
model-00002-of-00002.safetensors
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2460f61d1cba7f0a980bcfb8711f24fea25f0a7a09dbbb689fb4c750dd1154f0
|
3 |
+
size 456578416
|
model.safetensors.index.json
ADDED
@@ -0,0 +1,370 @@
|
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modeling_minicpm.py
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