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README.md

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-# bge-reranker-v2-minicpm-layerwise_a13590573183266816374692
+---
+license: apache-2.0
+pipeline_tag: text-classification
+tags:
+- transformers
+- sentence-transformers
+language:
+- multilingual
+---
 
-bge-reranker-v2-minicpm-layerwise
+# Reranker
+
+**More details please refer to our Github: [FlagEmbedding](https://github.com/FlagOpen/FlagEmbedding/tree/master).**
+
+- [Model List](#model-list)
+- [Usage](#usage)
+- [Fine-tuning](#fine-tune)
+- [Evaluation](#evaluation)
+- [Citation](#citation)
+
+Different from embedding model, reranker uses question and document as input and directly output similarity instead of embedding. 
+You can get a relevance score by inputting query and passage to the reranker. 
+And the score can be mapped to a float value in [0,1] by sigmoid function.
+
+
+## Model List
+
+| Model                                                                     | Base model                                                           | Language | layerwise |                           feature                            |
+|:--------------------------------------------------------------------------|:--------:|:-----------------------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------:|
+| [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) | [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) | Chinese and English |     -     | Lightweight reranker model, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) | [xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) | Chinese and English |     -     | Lightweight reranker model, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) | [bge-m3](https://huggingface.co/BAAI/bge-m3) |    Multilingual     |     -     | Lightweight reranker model, possesses strong multilingual capabilities, easy to deploy, with fast inference. |
+| [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma) |      [gemma-2b](https://huggingface.co/google/gemma-2b)      |    Multilingual     |     -     | Suitable for multilingual contexts, performs well in both English proficiency and multilingual capabilities. |
+| [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) | [MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) |    Multilingual     |   8-40    | Suitable for multilingual contexts, performs well in both English and Chinese proficiency, allows freedom to select layers for output, facilitating accelerated inference. |
+
+
+You can select the model according your senario and resource. 
+- For **multilingual**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)
+
+- For **Chinese or English**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). 
+
+- For **efficiency**, utilize [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) and the low layer of [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise). 
+
+- For better performance, recommand [BAAI/bge-reranker-v2-minicpm-layerwise](https://huggingface.co/BAAI/bge-reranker-v2-minicpm-layerwise) and [BAAI/bge-reranker-v2-gemma](https://huggingface.co/BAAI/bge-reranker-v2-gemma)
+
+## Usage 
+### Using FlagEmbedding
+
+```
+pip install -U FlagEmbedding
+```
+
+#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
+
+Get relevance scores (higher scores indicate more relevance):
+
+```python
+from FlagEmbedding import FlagReranker
+reranker = FlagReranker('BAAI/bge-reranker-v2-m3', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score) # -5.65234375
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+score = reranker.compute_score(['query', 'passage'], normalize=True)
+print(score) # 0.003497010252573502
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores) # [-8.1875, 5.26171875]
+
+# You can map the scores into 0-1 by set "normalize=True", which will apply sigmoid function to the score
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], normalize=True)
+print(scores) # [0.00027803096387751553, 0.9948403768236574]
+```
+
+#### For LLM-based reranker
+
+```python
+from FlagEmbedding import FlagLLMReranker
+reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = FlagLLMReranker('BAAI/bge-reranker-v2-gemma', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'])
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']])
+print(scores)
+```
+
+#### For LLM-based layerwise reranker
+
+```python
+from FlagEmbedding import LayerWiseFlagLLMReranker
+reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
+# reranker = LayerWiseFlagLLMReranker('BAAI/bge-reranker-v2-minicpm-layerwise', use_bf16=True) # You can also set use_bf16=True to speed up computation with a slight performance degradation
+
+score = reranker.compute_score(['query', 'passage'], cutoff_layers=[28]) # Adjusting 'cutoff_layers' to pick which layers are used for computing the score.
+print(score)
+
+scores = reranker.compute_score([['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']], cutoff_layers=[28])
+print(scores)
+```
+
+### Using Huggingface transformers
+
+#### For normal reranker (bge-reranker-base / bge-reranker-large / bge-reranker-v2-m3 )
+
+Get relevance scores (higher scores indicate more relevance):
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-m3')
+model = AutoModelForSequenceClassification.from_pretrained('BAAI/bge-reranker-v2-m3')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors='pt', max_length=512)
+    scores = model(**inputs, return_dict=True).logits.view(-1, ).float()
+    print(scores)
+```
+
+#### For LLM-based reranker
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
+    if prompt is None:
+        prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."
+    sep = "\n"
+    prompt_inputs = tokenizer(prompt,
+                              return_tensors=None,
+                              add_special_tokens=False)['input_ids']
+    sep_inputs = tokenizer(sep,
+                           return_tensors=None,
+                           add_special_tokens=False)['input_ids']
+    inputs = []
+    for query, passage in pairs:
+        query_inputs = tokenizer(f'A: {query}',
+                                 return_tensors=None,
+                                 add_special_tokens=False,
+                                 max_length=max_length * 3 // 4,
+                                 truncation=True)
+        passage_inputs = tokenizer(f'B: {passage}',
+                                   return_tensors=None,
+                                   add_special_tokens=False,
+                                   max_length=max_length,
+                                   truncation=True)
+        item = tokenizer.prepare_for_model(
+            [tokenizer.bos_token_id] + query_inputs['input_ids'],
+            sep_inputs + passage_inputs['input_ids'],
+            truncation='only_second',
+            max_length=max_length,
+            padding=False,
+            return_attention_mask=False,
+            return_token_type_ids=False,
+            add_special_tokens=False
+        )
+        item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
+        item['attention_mask'] = [1] * len(item['input_ids'])
+        inputs.append(item)
+    return tokenizer.pad(
+            inputs,
+            padding=True,
+            max_length=max_length + len(sep_inputs) + len(prompt_inputs),
+            pad_to_multiple_of=8,
+            return_tensors='pt',
+    )
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-gemma')
+model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-gemma')
+yes_loc = tokenizer('Yes', add_special_tokens=False)['input_ids'][0]
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = get_inputs(pairs, tokenizer)
+    scores = model(**inputs, return_dict=True).logits[:, -1, yes_loc].view(-1, ).float()
+    print(scores)
+```
+
+#### For LLM-based layerwise reranker
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def get_inputs(pairs, tokenizer, prompt=None, max_length=1024):
+    if prompt is None:
+        prompt = "Given a query A and a passage B, determine whether the passage contains an answer to the query by providing a prediction of either 'Yes' or 'No'."
+    sep = "\n"
+    prompt_inputs = tokenizer(prompt,
+                              return_tensors=None,
+                              add_special_tokens=False)['input_ids']
+    sep_inputs = tokenizer(sep,
+                           return_tensors=None,
+                           add_special_tokens=False)['input_ids']
+    inputs = []
+    for query, passage in pairs:
+        query_inputs = tokenizer(f'A: {query}',
+                                 return_tensors=None,
+                                 add_special_tokens=False,
+                                 max_length=max_length * 3 // 4,
+                                 truncation=True)
+        passage_inputs = tokenizer(f'B: {passage}',
+                                   return_tensors=None,
+                                   add_special_tokens=False,
+                                   max_length=max_length,
+                                   truncation=True)
+        item = tokenizer.prepare_for_model(
+            [tokenizer.bos_token_id] + query_inputs['input_ids'],
+            sep_inputs + passage_inputs['input_ids'],
+            truncation='only_second',
+            max_length=max_length,
+            padding=False,
+            return_attention_mask=False,
+            return_token_type_ids=False,
+            add_special_tokens=False
+        )
+        item['input_ids'] = item['input_ids'] + sep_inputs + prompt_inputs
+        item['attention_mask'] = [1] * len(item['input_ids'])
+        inputs.append(item)
+    return tokenizer.pad(
+            inputs,
+            padding=True,
+            max_length=max_length + len(sep_inputs) + len(prompt_inputs),
+            pad_to_multiple_of=8,
+            return_tensors='pt',
+    )
+
+tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained('BAAI/bge-reranker-v2-minicpm-layerwise', trust_remote_code=True, torch_dtype=torch.bfloat16)
+model = model.to('cuda')
+model.eval()
+
+pairs = [['what is panda?', 'hi'], ['what is panda?', 'The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.']]
+with torch.no_grad():
+    inputs = get_inputs(pairs, tokenizer).to(model.device)
+    all_scores = model(**inputs, return_dict=True, cutoff_layers=[28])
+    all_scores = [scores[:, -1].view(-1, ).float() for scores in all_scores[0]]
+    print(all_scores)
+```
+
+## Fine-tune
+
+### Data Format
+
+Train data should be a json file, where each line is a dict like this:
+
+```
+{"query": str, "pos": List[str], "neg":List[str], "prompt": str}
+```
+
+`query` is the query, and `pos` is a list of positive texts, `neg` is a list of negative texts, `prompt` indicates the relationship between query and texts. If you have no negative texts for a query, you can random sample some from the entire corpus as the negatives.
+
+See [toy_finetune_data.jsonl](https://github.com/FlagOpen/FlagEmbedding/tree/master/FlagEmbedding/llm_reranker/toy_finetune_data.jsonl) for a toy data file.
+
+### Train
+
+You can fine-tune the reranker with the following code:
+
+**For llm-based reranker**
+
+```shell
+torchrun --nproc_per_node {number of gpus} \
+-m FlagEmbedding.llm_reranker.finetune_for_instruction.run \
+--output_dir {path to save model} \
+--model_name_or_path google/gemma-2b \
+--train_data ./toy_finetune_data.jsonl \
+--learning_rate 2e-4 \
+--num_train_epochs 1 \
+--per_device_train_batch_size 1 \
+--gradient_accumulation_steps 16 \
+--dataloader_drop_last True \
+--query_max_len 512 \
+--passage_max_len 512 \
+--train_group_size 16 \
+--logging_steps 1 \
+--save_steps 2000 \
+--save_total_limit 50 \
+--ddp_find_unused_parameters False \
+--gradient_checkpointing \
+--deepspeed stage1.json \
+--warmup_ratio 0.1 \
+--bf16 \
+--use_lora True \
+--lora_rank 32 \
+--lora_alpha 64 \
+--use_flash_attn True \
+--target_modules q_proj k_proj v_proj o_proj
+```
+
+**For llm-based layerwise reranker**
+
+```shell
+torchrun --nproc_per_node {number of gpus} \
+-m FlagEmbedding.llm_reranker.finetune_for_layerwise.run \
+--output_dir {path to save model} \
+--model_name_or_path openbmb/MiniCPM-2B-dpo-bf16 \
+--train_data ./toy_finetune_data.jsonl \
+--learning_rate 2e-4 \
+--num_train_epochs 1 \
+--per_device_train_batch_size 1 \
+--gradient_accumulation_steps 16 \
+--dataloader_drop_last True \
+--query_max_len 512 \
+--passage_max_len 512 \
+--train_group_size 16 \
+--logging_steps 1 \
+--save_steps 2000 \
+--save_total_limit 50 \
+--ddp_find_unused_parameters False \
+--gradient_checkpointing \
+--deepspeed stage1.json \
+--warmup_ratio 0.1 \
+--bf16 \
+--use_lora True \
+--lora_rank 32 \
+--lora_alpha 64 \
+--use_flash_attn True \
+--target_modules q_proj k_proj v_proj o_proj \
+--start_layer 8 \
+--head_multi True \
+--head_type simple \
+--lora_extra_parameters linear_head
+```
+
+Our rerankers are initialized from [google/gemma-2b](https://huggingface.co/google/gemma-2b) (for llm-based reranker) and [openbmb/MiniCPM-2B-dpo-bf16](https://huggingface.co/openbmb/MiniCPM-2B-dpo-bf16) (for llm-based layerwise reranker), and we train it on a mixture of multilingual datasets:
+
+- [bge-m3-data](https://huggingface.co/datasets/Shitao/bge-m3-data)
+- [quora train data](https://huggingface.co/datasets/quora)
+- [fever train data](https://fever.ai/dataset/fever.html)
+
+## Evaluation
+
+- llama-index.
+
+![image-20240317193909373](./assets/llama-index.png)
+
+
+- BEIR.   
+
+rereank the top 100 results from bge-en-v1.5 large.
+
+![image-20240317174633333](./assets/BEIR-bge-en-v1.5.png)
+
+rereank the top 100 results from e5 mistral 7b instruct.
+
+![image-20240317172949713](./assets/BEIR-e5-mistral.png)
+
+- CMTEB-retrieval.   
+It rereank the top 100 results from bge-zh-v1.5 large.
+
+![image-20240317173026235](./assets/CMTEB-retrieval-bge-zh-v1.5.png)
+
+- miracl (multi-language).   
+It rereank the top 100 results from bge-m3.
+
+![image-20240317173117639](./assets/miracl-bge-m3.png)
+
+
+
+## Citation
+
+If you find this repository useful, please consider giving a star and citation
+
+```bibtex
+@misc{li2023making,
+      title={Making Large Language Models A Better Foundation For Dense Retrieval}, 
+      author={Chaofan Li and Zheng Liu and Shitao Xiao and Yingxia Shao},
+      year={2023},
+      eprint={2312.15503},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+@misc{chen2024bge,
+      title={BGE M3-Embedding: Multi-Lingual, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation}, 
+      author={Jianlv Chen and Shitao Xiao and Peitian Zhang and Kun Luo and Defu Lian and Zheng Liu},
+      year={2024},
+      eprint={2402.03216},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,38 @@
+{
+  "_name_or_path": "BAAI/bge-reranker-v2-minicpm-layerwise",
+  "architectures": [
+    "LayerWiseMiniCPMForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "BAAI/bge-reranker-v2-minicpm-layerwise--configuration_minicpm_reranker.LayerWiseMiniCPMConfig",
+    "AutoModel": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMModel",
+    "AutoModelForCausalLM": "BAAI/bge-reranker-v2-minicpm-layerwise--modeling_minicpm_reranker.LayerWiseMiniCPMForCausalLM"
+  },
+  "bos_token_id": 1,
+  "dim_model_base": 256,
+  "eos_token_id": 2,
+  "head_multi": true,
+  "head_type": "simple",
+  "hidden_act": "silu",
+  "hidden_size": 2304,
+  "initializer_range": 0.1,
+  "intermediate_size": 5760,
+  "max_position_embeddings": 2048,
+  "model_type": "minicpm",
+  "num_attention_heads": 36,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 36,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "scale_depth": 1.4,
+  "scale_emb": 12,
+  "start_layer": 8,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.38.1",
+  "use_cache": false,
+  "vocab_size": 122753
+}

configuration.json

@@ -0,0 +1 @@
+{"framework": "pytorch", "task": "text-classification", "allow_remote": true}

configuration_minicpm_reranker.py

@@ -0,0 +1,209 @@
+# 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 LayerWiseMiniCPMConfig(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,
+        start_layer=8,
+        head_multi=True,
+        head_type="simple",
+        **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.start_layer = start_layer
+        self.head_multi = head_multi
+        self.head_type = head_type
+
+        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}")

generation_config.json

@@ -0,0 +1,8 @@
+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "temperature": 0.8,
+  "top_p": 0.8,
+  "transformers_version": "4.38.1"
+}

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+    "model.layers.9.post_attention_layernorm.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.k_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.o_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.q_proj.weight": "model-00001-of-00003.safetensors",
+    "model.layers.9.self_attn.v_proj.weight": "model-00001-of-00003.safetensors",
+    "model.norm.weight": "model-00003-of-00003.safetensors"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,42 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "chat_template": "{% for message in messages %}{% if message['role'] == 'user' %}{{'<用户>' + message['content'].strip() + '<AI>'}}{% else %}{{message['content'].strip()}}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<unk>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}