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Model: BAAI/AquilaChat-7B
Source: Original Platform

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README.md

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+---
+license: other
+---
+
+
+![Aquila_logo](./log.jpeg)
+
+
+<h4 align="center">
+    <p>
+        <b>English</b> |
+        <a href="https://huggingface.co/BAAI/AquilaChat-7B/blob/main/README_zh.md">简体中文</a> 
+    </p>
+</h4>
+
+Aquila Language Model is the first open source language model that supports both Chinese and English knowledge, commercial license agreements, and compliance with domestic data regulations.
+
+- 🌟 **Supports open source commercial licenses**. The source code of the Aquila series models is based on the [Apache 2.0 agreement](https://www.apache.org/licenses/LICENSE-2.0), while the model weight is based on the [BAAI Aquila Model License Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf). Users can use it for commercial purposes as long as they meet the licensing restrictions.
+
+- ✍️ **Possesses Chinese and English knowledge**. The Aquila series model is trained from scratch on a high-quality corpus of Chinese and English languages, with Chinese corpora accounting for about 40%, ensuring that the model accumulates native Chinese world knowledge during the pre-training phase, rather than translated knowledge.
+
+- 👮‍♀️ **Complies with domestic data regulations**. The Chinese corpora of the Aquila series models come from Intelligence Source's accumulated Chinese datasets over the years, including Chinese internet data from over 10,000 sources (more than 99% of which are domestic sources), as well as high-quality Chinese literature and book data supported by authoritative domestic organizations. We will continue to accumulate high-quality and diverse datasets and incorporate them into the subsequent training of the Aquila base models.
+
+- 🎯 **Continuous improvements and open sourcing**. We will continue to improve training data, optimize training methods, and enhance model performance, cultivate a flourishing "model tree" on a better base model foundation, and continuously update open-source versions.
+
+The additional details of the Aquila model will be presented in the official technical report. Please stay tuned for updates on official channels, including the [FlagAI GitHub repository](https://github.com/FlagAI-Open/FlagAI/), [FlagAI's Zhihu account](https://www.zhihu.com/people/95-22-20-18) and [FlagAI's official technical communication group](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg).
+
+
+| Model              | Model Type               | Description                                                                                                                                                                                                                                                                                                                                                                                                     | Status         | GPUs Used    |
+| :----------------- | :----------------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | :--------------| :----------- | 
+| Aquila-7B          | Base model, 7 billion parameters   | **Aquila Base Model** inherits the architectural design advantages of GPT-3 and LLaMA. It replaces a batch of more efficient underlying operator implementations, redesigns the implementation of bilingual tokenizer, upgrades BMTrain parallel training method, and achieves nearly 8 times the training efficiency of Magtron+DeepSpeed ZeRO-2.                                   | Released       | Nvidia-A100   |
+| Aquila-33B         | Base model, 33 billion parameters   | Same as above                                                                                                                                                                                                                                                                                                                                                                        | Coming soon                                               | Nvidia-A100   |
+| AquilaChat-7B      | SFT model, fine-tuned and RL based on Aquila-7B  | **AquilaChat Dialog Model** supports fluent text dialogue and multiple language generation tasks, and realizes the call of AquilaChat to other models and tools by defining an expandable special instruction specification, which is easy to extend. For example, calling the open source **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) multimodal language image generation model** of Flagship Intelligence achieved smooth image generation capability. Together with Flagship Intelligence's **InstructFace multi-step controllable text-picture model**, it is easy to achieve multi-step controllable editing of human face images. | Released    | Nvidia-A100   |
+| AquilaChat-33B     | SFT model, fine-tuned and RL based on Aquila-33B  | Same as above                                                                                                                                                                                                                                                                                                                                                                                                   | Coming soon                                               | Nvidia-A100   |
+| AquilaCode-7B-NV   | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Nvidia  | AquilaCode-7B achieves high performance with small data sets and parameters, and is currently the best open source code model that supports both Chinese and English, trained using training code data with compliant open source licenses after high-quality filtering. AquilaCode-7B has been trained on both Nvidia and domestic chips for code models. | Released on GitHub | Nvidia-A100  |
+| AquilaCode-7B-TS   | Base model, "text-code" generation model, further pre-trained based on Aquila-7B, trained on Horizon Robotics chips | Same as above                                                                                                                                                                                                                                                                                                                                                                                                             | Released on GitHub       | Tianshu-BI-V100 |
+
+
+We will continue to release improved versions of Aquila model as open source. 
+
+- 2023/08/15 ：release v0.10
+  - Aquila-7B-01  md5: 4279db72e68df1a0705ecc8d4c7be3db
+  - Aquila-7B-02  md5: 621f8ce4c8deebe1635f5a09aa4b80f2
+  - AquilaChat-7B-01 md5: 22b22ffaed51388ce23f8e328a9b6a18
+  - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4
+
+n the FlagEval large model evaluation ("Subjective + Objective"), AquilaChat-7B v1.0 has shown a slight overall improvement compared to last version. It achieved an improvement of around 12.46% on the C-Eval, 10.88% on the MMLU, and 9.93% on the BoolQ dataset. 
+For detailed evaluation results, please refer to the website http://flageval.baai.ac.cn. For detailed version change history, see [Change Log](https://huggingface.co/BAAI/AquilaChat-7B/blob/main/change_log.log).
+
+<!-- </table>  -->
+
+## Quick Start  AquilaChat-7B（Chat model）
+
+### 1. Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+device = torch.device("cuda")
+model_info = "BAAI/AquilaChat-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
+model.eval()
+model.to(device)
+text = "请给出10个要到北京旅游的理由。"
+tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
+tokens = torch.tensor(tokens)[None,].to(device)
+stop_tokens = ["###", "[UNK]", "</s>"]
+with torch.no_grad():
+    out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007, bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
+    out = tokenizer.decode(out.cpu().numpy().tolist())
+    print(out)
+```
+
+
+## License
+
+AquilaChat-7B and AquilaChat-33B open-source model is licensed under [ BAAI Aquila Model Licence Agreement](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)
+
+
+
+
+# Acknowledgements
+
+This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300).
+本项目受新一代人工智能国家科技重大专项（No. 2022ZD0116300）支持。

README_zh.md

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+---
+license: other
+---
+
+![Aquila_logo](./log.jpeg)
+
+# 悟道·天鹰（Aquila）
+
+悟道·天鹰（Aquila） 语言大模型是首个具备中英双语知识、支持商用许可协议、国内数据合规需求的开源语言大模型。
+- 🌟 **支持开源商用许可**。Aquila系列模型的源代码基于 [Apache 2.0 协议](https://www.apache.org/licenses/LICENSE-2.0)，模型权重基于[《智源Aquila系列模型许可协议》](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)，使用者在满足许可限制的情况下，可用于商业目的。
+- ✍️ **具备中英文知识**。Aquila系列模型在中英文高质量语料基础上从 0 开始训练，中文语料约占 40%，保证模型在预训练阶段就开始积累原生的中文世界知识，而非翻译而来的知识。
+- 👮‍♀️**符合国内数据合规需求**。Aquila系列模型的中文语料来自智源多年积累的中文数据集，包括来自1万多个站源的中文互联网数据（其中99%以上为国内站源），以及获得国内权威机构支持的高质量中文文献数据、中文书籍数据等。我们仍在持续积累高质量、多样化的数据集，并源源不断加入Aquila基础模型后续训练中。
+- 🎯**持续迭代，持续开源开放**。我们将不断完善训练数据、优化训练方法、提升模型性能，在更优秀的基础模型基座上，培育枝繁叶茂的“模型树”，持续开源开放更新的版本。
+
+
+
+悟道 · 天鹰 Aquila 模型的更多细节将在官方技术报告中呈现。请关注官方渠道更新。包括 [FlagAI GitHub仓库](https://github.com/FlagAI-Open/FlagAI/)，[FlagAI 知乎账号](https://www.zhihu.com/people/95-22-20-18)、[FlagAI 官方技术交流群](https://github.com/FlagAI-Open/FlagAI/blob/master/wechat-qrcode.jpg)、智源研究院微信公众号、智源社区微信公众号。
+
+
+|   模型          |  模型类型    | 简介  |  状态   |  训练所用显卡   |                                   
+| :---------------- | :------- | :-- | :-- | :-- | 
+| Aquila-7B         | 基础模型，70亿参数  |   **Aquila 基础模型**在技术上继承了 GPT-3、LLaMA 等的架构设计优点，替换了一批更高效的底层算子实现、重新设计实现了中英双语的 tokenizer，升级了 BMTrain 并行训练方法，实现了比 Magtron+DeepSpeed ZeRO-2 将近８倍的训练效率。   | 已发布 | Nvidia-A100 |
+| Aquila-33B          |基础模型，330亿参数  |    同上    | **敬请期待** | Nvidia-A100 | 
+| AquilaChat-7B          |SFT model，基于 Aquila-7B 进行微调和强化学习  |    **AquilaChat 对话模型**支持流畅的文本对话及多种语言类生成任务，通过定义可扩展的特殊指令规范，实现 AquilaChat对其它模型和工具的调用，且易于扩展。 <br><br>例如，调用智源开源的 **[AltDiffusion](https://github.com/FlagAI-Open/FlagAI/tree/master/examples/AltDiffusion-m18) 多语言文图生成模型**，实现了流畅的文图生成能力。配合智源 **InstructFace 多步可控文生图模型**，轻松实现对人脸图像的多步可控编辑。  | 已发布  | Nvidia-A100  | 
+| AquilaChat-33B           |SFT model，基于 Aquila-33B 进行微调和强化学习 |   同上    | **敬请期待** | Nvidia-A100 | 
+| AquilaCode-7B-NV          | 基础模型，“文本-代码”生成模型，基于 Aquila-7B继续预训练，在英伟达芯片完成训练  |   AquilaCode-7B 以小数据集、小参数量，实现高性能，是目前支持中英双语的、性能最好的开源代码模型，经过了高质量过滤、使用有合规开源许可的训练代码数据进行训练。<br><br> AquilaCode-7B 分别在英伟达和国产芯片上完成了代码模型的训练。  | github已发布  | Nvidia-A100 | 
+| AquilaCode-7B-TS           |基础模型，“文本-代码”生成模型，基于 Aquila-7B继续预训练，在天数智芯芯片上完成训练  |    同上    | github已发布  | Tianshu-BI-V100  |
+
+悟道·天鹰Aquila系列模型将持续开源更优版本。
+
+- 2023/08/15 ：开源 v0.10
+  - Aquila-7B-01  md5: 4279db72e68df1a0705ecc8d4c7be3db
+  - Aquila-7B-02  md5: 621f8ce4c8deebe1635f5a09aa4b80f2
+  - AquilaChat-7B-01 md5: 22b22ffaed51388ce23f8e328a9b6a18
+  - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4
+
+其中AquilaChat-7B新版本模型在 FlagEval 大模型评测中（ “客观”）相比上一版在C-Eval、MMLU、BoolQ上分别提升12.46%，10.88%和9.93%。
+详细评测结果请通过 http://flageval.baai.ac.cn 网站查看。历史版本变更详情见：[变更日志](https://huggingface.co/BAAI/AquilaChat-7B/blob/main/change_log.log)。
+
+## 快速开始使用 AquilaChat-7B 对话模型
+
+
+## 使用方式/How to use
+
+### 1. 推理/Inference
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+device = torch.device("cuda")
+model_info = "BAAI/AquilaChat-7B"
+tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True)
+model.eval()
+model.to(device)
+text = "请给出10个要到北京旅游的理由。"
+tokens = tokenizer.encode_plus(text)['input_ids'][:-1]
+tokens = torch.tensor(tokens)[None,].to(device)
+stop_tokens = ["###", "[UNK]", "</s>"]
+with torch.no_grad():
+    out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007, bad_words_ids=[[tokenizer.encode(token)[0] for token in stop_tokens]])[0]
+    out = tokenizer.decode(out.cpu().numpy().tolist())
+    print(out)
+```
+
+
+## 证书/License
+
+`AquilaChat`系列开源模型使用 [智源Aquila系列模型许可协议](https://huggingface.co/BAAI/AquilaChat-7B/resolve/main/BAAI%20Aquila%20Model%20License%20Agreement.pdf)

change_log.log

@@ -0,0 +1,15 @@
+- 2023/08/15 ：发布权重文件 v0.10，开源了 Aquila-7B、AquilaChat-7B 最新权重
+  - Aquila-7B-01  md5: 4279db72e68df1a0705ecc8d4c7be3db
+  - Aquila-7B-02  md5: 621f8ce4c8deebe1635f5a09aa4b80f2
+  - AquilaChat-7B-01 md5: 22b22ffaed51388ce23f8e328a9b6a18
+  - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4
+- 2023/07/14 ：发布权重文件 v0.8，开源了 Aquila-7B、AquilaChat-7B 最新权重
+  - Aquila-7B-01  md5: b14329f7314c05dd79d44b2838c315aa
+  - Aquila-7B-02  md5: 88aa286283c7b7dd78c0fbb7fae6327d
+  - AquilaChat-7B-01 md5: 0a77901af35d3e5ed16eeafa622e2173
+  - AquilaChat-7B-02 md5: 6e84423fe2837c79c0ced6817c316bd4
+- 2023/07/07 ：发布权重文件 v0.7，开源了 Aquila-7B、AquilaChat-7B 最新权重
+  - Aquila-7B-01  md5: add74c012a03bc6dfcd66108554299cf
+  - Aquila-7B-02  md5: 8a9b7a63ac8df085bc7b18f557c1d322
+  - AquilaChat-7B-01 md5: e8fad926dfbb3601220338154caa4717
+  - AquilaChat-7B-02 md5: e24386be3a62eeb360997481559fd48b

chat_test.py

@@ -0,0 +1,23 @@
+#If you need to use this code, please install the following transformers
+#https://github.com/shunxing1234/transformers
+from transformers import AutoTokenizer, AquilaForCausalLM
+import torch
+from cyg_conversation import default_conversation, covert_prompt_to_input_ids_with_history
+
+tokenizer = AutoTokenizer.from_pretrained("BAAI/AquilaChat-7B")
+model = AquilaForCausalLM.from_pretrained("BAAI/AquilaChat-7B")
+model.eval()
+model.to("cuda:4")
+vocab = tokenizer.vocab
+print(len(vocab))
+
+text = "请给出10个要到北京旅游的理由。"
+
+tokens = covert_prompt_to_input_ids_with_history(text, history=[], tokenizer=tokenizer, max_token=512)
+
+tokens = torch.tensor(tokens)[None,].to("cuda:4")
+
+out = model.generate(tokens, do_sample=True, max_length=512, eos_token_id=100007)[0]
+out = tokenizer.decode(out.cpu().numpy().tolist())
+
+print(out)

config.json

@@ -0,0 +1,28 @@
+{
+  "_name_or_path": "BAAI/AquilaChat-7B ",
+  "model_type": "aquila",
+  "architectures": [
+    "AquilaModel"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_aquila.AquilaConfig",
+    "AutoModelForCausalLM": "modeling_aquila.AquilaForCausalLM"
+  },
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "aquila",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "pad_token_id": 0,
+  "rms_norm_eps": 1e-05,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "use_cache": true,
+  "vocab_size": 100008
+}

configuration.json

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

configuration_aquila.py

@@ -0,0 +1,128 @@
+# coding=utf-8
+# Copyright 2023 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.
+""" Aquila model configuration"""
+
+from transformers import PretrainedConfig
+
+
+
+class AquilaConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`AquilaModel`]. It is used to instantiate an Aquila
+    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 Aquila-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 Aquila model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`AquilaModel`]
+        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 encoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        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. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        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-12):
+            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`.
+        tie_word_embeddings(`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        Example:
+
+    ```python
+    >>> from transformers import AquilaModel, AquilaConfig
+
+    >>> # Initializing a Aquila aquila-7b style configuration
+    >>> configuration = AquilaConfig()
+
+    >>> # Initializing a model from the aquila-7b style configuration
+    >>> model = AquilaModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "aquila"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=100008,
+        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=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        **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
+
+        # 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.num_attention_heads = num_attention_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
+
+        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,
+        )

cyg_conversation.py

@@ -0,0 +1,155 @@
+import dataclasses
+from enum import auto, Enum
+from typing import List, Tuple, Any
+
+
+class SeparatorStyle(Enum):
+    """Different separator style."""
+    SINGLE = auto()
+    TWO = auto()
+
+
+@dataclasses.dataclass
+class Conversation:
+    """A class that keeps all conversation history."""
+    system: str
+    instruction: str
+    roles: List[str]
+    messages: List[List[str]]
+    offset: int
+    sep_style: SeparatorStyle = SeparatorStyle.SINGLE
+    sep: str = "###"
+    sep2: str = None
+
+    skip_next: bool = False
+    conv_id: Any = None
+
+    def get_prompt(self):
+        if self.sep_style == SeparatorStyle.SINGLE:
+            ret = self.system + self.sep
+            if self.instruction is not None and len(self.instruction) > 0:
+                ret += self.roles[2] + ": " + self.instruction + self.sep
+            for role, message in self.messages:
+                if message:
+                    ret += role + ": " + message + self.sep
+                else:
+                    ret += role + ":"
+            return ret
+        elif self.sep_style == SeparatorStyle.TWO:
+            seps = [self.sep, self.sep2]
+            ret = self.system + seps[0]
+            if self.instruction is not None and len(self.instruction) > 0:
+                ret += self.roles[2] + ": " + self.instruction + self.sep
+            for i, (role, message) in enumerate(self.messages):
+                if message:
+                    ret += role + ": " + message + seps[i % 2]
+                else:
+                    ret += role + ":"
+            return ret
+        else:
+            raise ValueError(f"Invalid style: {self.sep_style}")
+
+    def append_message(self, role, message):
+        self.messages.append([role, message])
+
+    def to_gradio_chatbot(self):
+        ret = []
+        for i, (role, msg) in enumerate(self.messages[self.offset:]):
+            if i % 2 == 0:
+                ret.append([msg, None])
+            else:
+                ret[-1][-1] = msg
+        return ret
+
+    def copy(self):
+        return Conversation(
+            system=self.system,
+            instruction=self.instruction,
+            roles=self.roles,
+            messages=[[x, y] for x, y in self.messages],
+            offset=self.offset,
+            sep_style=self.sep_style,
+            sep=self.sep,
+            sep2=self.sep2,
+            conv_id=self.conv_id)
+
+    def dict(self):
+        return {
+            "system": self.system,
+            "instruction": self.instruction,
+            "roles": self.roles,
+            "messages": self.messages,
+            "offset": self.offset,
+            "sep": self.sep,
+            "sep2": self.sep2,
+            "conv_id": self.conv_id,
+        }
+
+
+conv_v1 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    instruction="",
+    roles=("Human", "Assistant", "System"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+conv_v1_2 = Conversation(
+    system="A chat between a curious human and an artificial intelligence assistant. "
+           "The assistant gives helpful, detailed, and polite answers to the human's questions.",
+    instruction="",
+    roles=("Human", "Assistant", "System"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.SINGLE,
+    sep="###",
+)
+
+conv_bair_v1 = Conversation(
+    system="BEGINNING OF CONVERSATION:",
+    instruction="",
+    roles=("USER", "GPT", "System"),
+    messages=(),
+    offset=0,
+    sep_style=SeparatorStyle.TWO,
+    sep=" ",
+    sep2="</s>",
+)
+
+
+default_conversation = conv_v1_2
+conv_templates = {
+    "v1": conv_v1_2,
+    "bair_v1": conv_bair_v1,
+}
+
+def covert_prompt_to_input_ids_with_history(text, history, tokenizer, max_token):
+    conv = default_conversation.copy()
+
+    conv.append_message(conv.roles[1], None)
+    conv.append_message(conv.roles[0], text)
+
+    example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
+
+    while(len(history) > 0 and (len(example) < max_token)):
+        tmp = history.pop()
+        if tmp[0] == 'ASSISTANT':
+            conv.append_message(conv.roles[1], tmp[1])
+        else:
+            conv.append_message(conv.roles[0], tmp[1])
+        example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
+
+    if len(example) >= max_token:
+        conv.messages.pop()
+    conv.messages = conv.messages[::-1]
+    print('model in:', conv.get_prompt())
+    example = tokenizer.encode_plus(f"{conv.get_prompt()}", None, max_length=None)['input_ids']
+    example = example[1:-1]
+
+    return example
+
+if __name__ == "__main__":
+    print(default_conversation.get_prompt())

generation_config.json

@@ -0,0 +1,7 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.28.1"
+}

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