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Model: Shanghai_AI_Laboratory/internlm-20b Source: Original Platform
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+**InternLM**
+
+<div align="center">
+
+<img src="https://github.com/InternLM/InternLM/assets/22529082/b9788105-8892-4398-8b47-b513a292378e" width="200"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">InternLM</font></b>
+    <sup>
+      <a href="https://internlm.intern-ai.org.cn/">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    <div>&nbsp;</div>
+  </div>
+
+[![evaluation](https://github.com/InternLM/InternLM/assets/22529082/f80a2a58-5ddf-471a-8da4-32ab65c8fd3b)](https://github.com/internLM/OpenCompass/)
+
+[💻Github Repo](https://github.com/InternLM/InternLM) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new)
+
+</div>
+
+
+## Introduction
+
+The Shanghai Artificial Intelligence Laboratory, in collaboration with SenseTime Technology, the Chinese University of Hong Kong, and Fudan University, has officially released the 20 billion parameter pretrained model, InternLM-20B. InternLM-20B was pre-trained on over **2.3T** Tokens containing high-quality English, Chinese, and code data. Additionally, the Chat version has undergone SFT and RLHF training, enabling it to better and more securely meet users' needs.
+
+In terms of model structure, InternLM-20B opted for a deeper architecture, with a depth set at 60 layers. This surpasses the conventional 7B and 13B models that utilize 32 or 40 layers. When parameters are limited, increasing the number of layers can enhance the model's overall capability. Furthermore, compared to InternLM-7B, the pre-training data used for InternLM-20B underwent higher quality cleansing and was supplemented with data rich in knowledge and designed for reinforcing understanding and reasoning capabilities. As a result, it exhibits significant improvements in understanding, reasoning, mathematical, and programming abilities—all of which test the technical proficiency of language models. Overall, InternLM-20B features the following characteristics:
+- Outstanding overall performance
+- Strong utility invocation capability
+- Supports a 16k context length (Through infererence extrapolation)
+- Better value alignment.
+
+## Performance Evaluation
+On the 5 capability dimensions proposed by OpenCompass, InternLM-20B has achieved excellent results (the bolded scores represent the best performances within the 13B-33B parameter range).
+
+| Capability | Llama-13B | Llama2-13B | Baichuan2-13B | InternLM-20B | Llama-33B | Llama-65B | Llama2-70B |
+|----------|-----------|------------|---------------|--------------|-----------|-----------|------------|
+| Language     | 42.5      | 47         | 47.5          | **55**           | 44.6      | 47.1      | 51.6       |
+| Knowledge     | 58.2      | 58.3       | 48.9          | 60.1         | **64**        | 66        | 67.7       |
+| Understanding     | 45.5      | 50.9       | 58.1          | **67.3**         | 50.6      | 54.2      | 60.8       |
+| Reasoning     | 42.7      | 43.6       | 44.2          | **54.9**         | 46.4      | 49.8      | 55         |
+| Examination     | 37.3      | 45.2       | 51.8          | **62.5**         | 47.4      | 49.7      | 57.3       |
+| Overall   | 43.8      | 47.3       | 49.4          | **59.2**         | 48.9      | 51.9      | 57.4       |
+
+The table below compares the performance of mainstream open-source models on some influential and typical datasets.
+
+|      | Benchmarks           | Llama-13B | Llama2-13B | Baichuan2-13B | InternLM-20B | Llama-33B | Llama-65B | Llama2-70B |
+|------|------------------|-----------|------------|---------------|--------------|-----------|-----------|------------|
+| Examination | MMLU             | 47.73     | 54.99      | 59.55         | **62.05**        | 58.73     | 63.71     | 69.75      |
+|      | C-Eval (val)     | 31.83     | 41.4       | **59.01**         | 58.8         | 37.47     | 40.36     | 50.13      |
+|      | AGI-Eval         | 22.03     | 30.93      | 37.37         | **44.58**        | 33.53     | 33.92     | 40.02      |
+| Knowledge | BoolQ            | 78.75     | 82.42      | 67            | **87.46**        | 84.43     | 86.61     | 87.74      |
+|      | TriviaQA         | 52.47     | 59.36      | 46.61         | 57.26        | **66.24**     | 69.79     | 70.71      |
+|      | NaturalQuestions | 20.17     | 24.85      | 16.32         | 25.15        | **30.89**     | 33.41     | 34.16      |
+| Understanding | CMRC             | 9.26      | 31.59      | 29.85         | **68.78**        | 14.17     | 34.73     | 43.74      |
+|      | CSL              | 55        | 58.75      | 63.12         | **65.62**        | 57.5      | 59.38     | 60         |
+|      | RACE (middle)    | 53.41     | 63.02      | 68.94         | **86.35**        | 64.55     | 72.35     | 81.55      |
+|      | RACE (high)      | 47.63     | 58.86      | 67.18         | **83.28**        | 62.61     | 68.01     | 79.93      |
+|      | XSum             | 20.37     | 23.37      | 25.23         | **35.54**        | 20.55     | 19.91     | 25.38      |
+| Reasoning | WinoGrande       | 64.64     | 64.01      | 67.32         | **69.38**        | 66.85     | 69.38     | 69.77      |
+|      | BBH              | 37.93     | 45.62      | 48.98         | **52.51**        | 49.98     | 58.38     | 64.91      |
+|      | GSM8K            | 20.32     | 29.57      | **52.62**         | **52.62**        | 42.3      | 54.44     | 63.31      |
+|      | PIQA             | 79.71     | 79.76      | 78.07         | 80.25        | **81.34**     | 82.15     | 82.54      |
+| Programming | HumanEval        | 14.02     | 18.9       | 17.07         | **25.61**        | 17.68     | 18.9      | 26.22      |
+|      | MBPP             | 20.6      | 26.8       | 30.8          | **35.6**         | 28.4      | 33.6      | 39.6       |
+
+Overall, InternLM-20B comprehensively outperforms open-source models in the 13B parameter range in terms of overall capabilities, and on inference evaluation sets, it approaches or even surpasses the performance of Llama-65B.
+
+## Import from Transformers
+To load the InternLM 20B model using Transformers, use the following code:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b", trust_remote_code=True)
+# Set `torch_dtype=torch.bfloat16` to load model in bfloat16, otherwise it will be loaded as float32 and cause OOM Error.
+model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b", torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
+model = model.eval()
+inputs = tokenizer(["Coming to the beautiful nature, we found"], return_tensors="pt")
+for k,v in inputs.items():
+    inputs[k] = v.cuda()
+gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.05}
+output = model.generate(**inputs, **gen_kwargs)
+output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
+print(output)
+# Coming to the beautiful nature, we found not only various mountains, rivers, trees, and flowers but also many birds and beasts. Birds are the ones we are most familiar with; some are soaring in the sky, some are hopping on the ground, while others perch on trees...
+```
+
+**Limitations:** Although we have made efforts to ensure the safety of the model during the training process and to encourage the model to generate text that complies with ethical and legal requirements, the model may still produce unexpected outputs due to its size and probabilistic generation paradigm. For example, the generated responses may contain biases, discrimination, or other harmful content. Please do not propagate such content. We are not responsible for any consequences resulting from the dissemination of harmful information.
+
+
+## Open Source License
+
+The code is licensed under Apache-2.0, while model weights are fully open for academic research and also allow **free** commercial usage. To apply for a commercial license, please fill in the [application form (English)](https://wj.qq.com/s2/12727483/5dba/)/[申请表（中文）](https://wj.qq.com/s2/12725412/f7c1/). For other questions or collaborations, please contact <internlm@pjlab.org.cn>.
+
+
+## 简介
+上海人工智能实验室与商汤科技联合香港中文大学和复旦大学正式推出书生·浦语200亿参数模型版本 InternLM-20B ，InternLM-20B 在超过 **2.3T** Tokens 包含高质量英文、中文和代码的数据上进行预训练，其中 Chat 版本还经过了 SFT 和 RLHF 训练，使其能够更好、更安全地满足用户的需求。  
+
+InternLM 20B 在模型结构上选择了深结构，层数设定为60层，超过常规7B和13B模型所使用的32层或者40层。在参数受限的情况下，提高层数有利于提高模型的综合能力。此外，相较于InternLM-7B，InternLM-20B使用的预训练数据经过了更高质量的清洗，并补充了高知识密度和用于强化理解与推理能力的训练数据。因此，它在理解能力、推理能力、数学能力、编程能力等考验语言模型技术水平的方面都得到了显著提升。总体而言，InternLM-20B具有以下的特点： 
+- 优异的综合性能
+- 很强的工具调用功能
+- 支持16k语境长度（通过推理时外推）
+- 更好的价值对齐
+
+## 性能评测
+在OpenCompass提出的5个能力维度上，InternLM-20B都取得很好的效果（粗体为13B-33B这个量级范围内，各项最佳成绩）
+
+| 能力维度 | Llama-13B | Llama2-13B | Baichuan2-13B | InternLM-20B | Llama-33B | Llama-65B | Llama2-70B |
+|----------|-----------|------------|---------------|--------------|-----------|-----------|------------|
+| 语言     | 42.5      | 47         | 47.5          | **55**           | 44.6      | 47.1      | 51.6       |
+| 知识     | 58.2      | 58.3       | 48.9          | 60.1         | **64**        | 66        | 67.7       |
+| 理解     | 45.5      | 50.9       | 58.1          | **67.3**         | 50.6      | 54.2      | 60.8       |
+| 推理     | 42.7      | 43.6       | 44.2          | **54.9**         | 46.4      | 49.8      | 55         |
+| 学科     | 37.3      | 45.2       | 51.8          | **62.5**         | 47.4      | 49.7      | 57.3       |
+| 总平均   | 43.8      | 47.3       | 49.4          | **59.2**         | 48.9      | 51.9      | 57.4       |
+
+下表展示了在多个经典数据集上 InternLM 20B 与各个主流开源模型的表现
+
+|      | 评测集           | Llama-13B | Llama2-13B | Baichuan2-13B | InternLM-20B | Llama-33B | Llama-65B | Llama2-70B |
+|------|------------------|-----------|------------|---------------|--------------|-----------|-----------|------------|
+| 学科 | MMLU             | 47.73     | 54.99      | 59.55         | **62.05**        | 58.73     | 63.71     | 69.75      |
+|      | C-Eval (val)     | 31.83     | 41.4       | **59.01**         | 58.8         | 37.47     | 40.36     | 50.13      |
+|      | AGI-Eval         | 22.03     | 30.93      | 37.37         | **44.58**        | 33.53     | 33.92     | 40.02      |
+| 知识 | BoolQ            | 78.75     | 82.42      | 67            | **87.46**        | 84.43     | 86.61     | 87.74      |
+|      | TriviaQA         | 52.47     | 59.36      | 46.61         | 57.26        | **66.24**     | 69.79     | 70.71      |
+|      | NaturalQuestions | 20.17     | 24.85      | 16.32         | 25.15        | **30.89**     | 33.41     | 34.16      |
+| 理解 | CMRC             | 9.26      | 31.59      | 29.85         | **68.78**        | 14.17     | 34.73     | 43.74      |
+|      | CSL              | 55        | 58.75      | 63.12         | **65.62**        | 57.5      | 59.38     | 60         |
+|      | RACE (middle)    | 53.41     | 63.02      | 68.94         | **86.35**        | 64.55     | 72.35     | 81.55      |
+|      | RACE (high)      | 47.63     | 58.86      | 67.18         | **83.28**        | 62.61     | 68.01     | 79.93      |
+|      | XSum             | 20.37     | 23.37      | 25.23         | **35.54**        | 20.55     | 19.91     | 25.38      |
+| 推理 | WinoGrande       | 64.64     | 64.01      | 67.32         | **69.38**        | 66.85     | 69.38     | 69.77      |
+|      | BBH              | 37.93     | 45.62      | 48.98         | **52.51**        | 49.98     | 58.38     | 64.91      |
+|      | GSM8K            | 20.32     | 29.57      | **52.62**         | **52.62**        | 42.3      | 54.44     | 63.31      |
+|      | PIQA             | 79.71     | 79.76      | 78.07         | 80.25        | **81.34**     | 82.15     | 82.54      |
+| 编程 | HumanEval        | 14.02     | 18.9       | 17.07         | **25.61**        | 17.68     | 18.9      | 26.22      |
+|      | MBPP             | 20.6      | 26.8       | 30.8          | **35.6**         | 28.4      | 33.6      | 39.6       |
+
+总体而言，InternLM-20B 在综合能力上全面领先于13B量级的开源模型，同时在推理评测集上能够接近甚至超越Llama-65B的性能。
+
+## 通过 Transformers 加载
+通过以下的代码加载 InternLM 20B 模型
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("internlm/internlm-20b", trust_remote_code=True)
+# `torch_dtype=torch.bfloat16` 可以令模型以 bfloat16 精度加载，否则 transformers 会将模型加载为 float32，导致显存不足
+model = AutoModelForCausalLM.from_pretrained("internlm/internlm-20b", torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
+model = model.eval()
+inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
+for k,v in inputs.items():
+    inputs[k] = v.cuda()
+gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.05}
+output = model.generate(**inputs, **gen_kwargs)
+output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
+print(output)
+# 来到美丽的大自然，我们发现，这里不仅有大大小小的山川河流和树木花草，而且还有很多飞鸟走兽。我们最熟悉的就是鸟类了，它们有的在天上飞翔，有的在地上跳跃，还有的在树上栖息……
+```
+
+**局限性：** 尽管在训练过程中我们非常注重模型的安全性，尽力促使模型输出符合伦理和法律要求的文本，但受限于模型大小以及概率生成范式，模型可能会产生各种不符合预期的输出，例如回复内容包含偏见、歧视等有害内容，请勿传播这些内容。由于传播不良信息导致的任何后果，本项目不承担责任。
+
+## 开源许可证
+
+本仓库的代码依照 Apache-2.0 协议开源。模型权重对学术研究完全开放，也可申请免费的商业使用授权（[申请表](https://wj.qq.com/s2/12725412/f7c1/)）。其他问题与合作请联系 <internlm@pjlab.org.cn>。
--- a/config.json
+++ b/config.json
@@ -0,0 +1,34 @@
+{
+  "architectures": [
+    "InternLMForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_internlm.InternLMConfig",
+    "AutoModel": "modeling_internlm.InternLMForCausalLM",
+    "AutoModelForCausalLM": "modeling_internlm.InternLMForCausalLM"
+  },
+  "bias": false,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 4096,
+  "model_type": "internlm",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 40,
+  "pad_token_id": 2,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.33.1",
+  "use_cache": true,
+  "vocab_size": 103168,
+  "rotary": {
+    "base": 10000,
+    "type": "dynamic"
+  }
+}
--- a/configuration.json
+++ b/configuration.json
@@ -0,0 +1,5 @@
+{
+    "framework": "pytorch",
+    "task": "text-generation",
+    "allow_remote": true
+}
--- a/configuration_internlm.py
+++ b/configuration_internlm.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/configuration_llama.py
+#
+# 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.
+""" InternLM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+# Modified from transformers.model.llama.configuration_llama.LlamaConfig
+class InternLMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate
+    an InternLM 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 InternLM-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 InternLM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`InternLMModel`]
+        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 InternLMModel, InternLMConfig
+    >>> # Initializing a InternLM internlm-7b style configuration
+    >>> configuration = InternLMConfig()
+    >>> # Initializing a model from the internlm-7b style configuration
+    >>> model = InternLMModel(configuration)
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+    model_type = "internlm"
+    _auto_class = "AutoConfig"
+
+    def __init__(  # pylint: disable=W0102
+        self,
+        vocab_size=103168,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        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,
+        tie_word_embeddings=False,
+        bias=True,
+        rotary={"base": 10000, "type": "dynamic"},  # pylint: disable=W0102
+        attn_implementation="eager",
+        **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
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.use_cache = use_cache
+        self.bias = bias
+        self.rotary = rotary
+        self.attn_implementation = attn_implementation
+        if self.attn_implementation is None:
+            self.attn_implementation = "eager"
+        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,
+        )
--- a/generation_config.json
+++ b/generation_config.json
@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.33.1"
+}
--- a/modeling_internlm.py
+++ b/modeling_internlm.py
--- a/pytorch_model-00001-of-00005.bin
+++ b/pytorch_model-00001-of-00005.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e3dfd4f2589dca958238e2d3ab78b439399336cd710af55cf179eea719744509
+size 9990638603
--- a/pytorch_model-00002-of-00005.bin
+++ b/pytorch_model-00002-of-00005.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:752da7c100d96caec94363ad06e1a52a1c4bf729a498f0d502b60d1f6b031e36
+size 9956584547
--- a/pytorch_model-00003-of-00005.bin
+++ b/pytorch_model-00003-of-00005.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a485b37af9ef3237c5869c33d12e47380ccb762772c2f1acd83898e45a34554
+size 9867476673
--- a/pytorch_model-00004-of-00005.bin
+++ b/pytorch_model-00004-of-00005.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9a9c69158c28d3a16c7b233e01c4290ccc544c716b5bb9dbb54abaf4ee858c4
+size 9306474783
--- a/pytorch_model-00005-of-00005.bin
+++ b/pytorch_model-00005-of-00005.bin
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d86f9f58ebf705e69d63aed00857daaa5ff45024727a322eff0600b62ef16d2
+size 1056441258
--- a/pytorch_model.bin.index.json
+++ b/pytorch_model.bin.index.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1924187067e58b62b951aae2fb004cf40844bdff987f5e50decfa8832d1f5cc3
+size 44754
--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,6 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "</s>",
+  "unk_token": "<unk>"
+}
--- a/tokenization_internlm.py
+++ b/tokenization_internlm.py
@@ -0,0 +1,237 @@
+# coding=utf-8
+# Copyright (c) The InternLM team and The HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on transformers/src/transformers/models/llama/tokenization_llama.py
+#
+# 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.
+
+"""Tokenization classes for InternLM."""
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {}
+
+# Modified from transformers.model.llama.tokenization_llama.LlamaTokenizer -> InternLM2Tokenizer
+class InternLMTokenizer(PreTrainedTokenizer):
+    """
+    Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    _auto_class = "AutoTokenizer"
+
+    def __init__(
+        self,
+        vocab_file,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="</s>",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        add_bos_token=True,
+        add_eos_token=False,
+        decode_with_prefix_space=False,
+        clean_up_tokenization_spaces=False,
+        **kwargs,
+    ):
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+        self.vocab_file = vocab_file
+        self.add_bos_token = add_bos_token
+        self.add_eos_token = add_eos_token
+        self.decode_with_prefix_space = decode_with_prefix_space
+        self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+        self.sp_model.Load(vocab_file)
+        self._no_prefix_space_tokens = None
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            **kwargs,
+        )
+
+    @property
+    def no_prefix_space_tokens(self):
+        if self._no_prefix_space_tokens is None:
+            vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
+            self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
+        return self._no_prefix_space_tokens
+
+    @property
+    def vocab_size(self):
+        """Returns vocab size"""
+        return self.sp_model.get_piece_size()
+
+    @property
+    def bos_token_id(self) -> Optional[int]:
+        return self.sp_model.bos_id()
+
+    @property
+    def eos_token_id(self) -> Optional[int]:
+        return self.sp_model.eos_id()
+
+    def get_vocab(self):
+        """Returns vocab as a dict"""
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.sp_model.piece_to_id(token)
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        token = self.sp_model.IdToPiece(index)
+        return token
+
+    def _maybe_add_prefix_space(self, tokens, decoded):
+        if tokens and tokens[0] not in self.no_prefix_space_tokens:
+            return " " + decoded
+        else:
+            return decoded
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (string) in a single string."""
+        current_sub_tokens = []
+        out_string = ""
+        prev_is_special = False
+        for token in tokens:
+            # make sure that special tokens are not decoded using sentencepiece model
+            if token in self.all_special_tokens:
+                if not prev_is_special:
+                    out_string += " "
+                out_string += self.sp_model.decode(current_sub_tokens) + token
+                prev_is_special = True
+                current_sub_tokens = []
+            else:
+                current_sub_tokens.append(token)
+                prev_is_special = False
+        out_string += self.sp_model.decode(current_sub_tokens)
+        out_string = self.clean_up_tokenization(out_string)
+        out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
+        return out_string[1:]
+
+    def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        out_vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+            copyfile(self.vocab_file, out_vocab_file)
+        elif not os.path.isfile(self.vocab_file):
+            with open(out_vocab_file, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (out_vocab_file,)
+
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+        if self.add_bos_token:
+            bos_token_ids = [self.bos_token_id]
+        else:
+            bos_token_ids = []
+
+        output = bos_token_ids + token_ids_0
+
+        if token_ids_1 is not None:
+            output = output + token_ids_1
+
+        if self.add_eos_token:
+            output = output + [self.eos_token_id]
+
+        return output
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        if token_ids_1 is None:
+            return [1] + ([0] * len(token_ids_0)) + [1]
+        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
+
+    def create_token_type_ids_from_sequences(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
+        use of token type ids, therefore a list of zeros is returned.
+
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+
+        Returns:
+            `List[int]`: List of zeros.
+        """
+        eos = [self.eos_token_id]
+
+        if token_ids_1 is None:
+            return len(token_ids_0 + eos) * [0]
+        return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
--- a/tokenizer.model
+++ b/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aab622d98c98677a1a51f969e25765154487bf3e85c7819db105db2fcacba83f
+size 1658691
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,15 @@
+{
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_internlm.InternLMTokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "tokenizer_class": "InternLMTokenizer",
+  "unk_token": "<unk>"
+}