Update modeling_internlm3.py (#18)

- Update modeling_internlm3.py (94cd46f35e87e1b3b2b82df73230bdb5275cd652)
- Update tokenization_internlm3.py (0f3d7019880c0b6f7a9d35b392d21cbfca07478b)

.gitattributes

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README.md

@@ -1,7 +1,7 @@
 ---
 license: apache-2.0
+pipeline_tag: text-generation
 ---
-
 # InternLM 
 
 
@@ -23,7 +23,7 @@ license: apache-2.0
 
 [![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) • [📜Technical Report](https://arxiv.org/abs/2403.17297)
+[💻Github Repo](https://github.com/InternLM/InternLM) • [🤗Demo](https://huggingface.co/spaces/internlm/internlm3-8b-instruct) • [🤔Reporting Issues](https://github.com/InternLM/InternLM/issues/new) • [📜Technical Report](https://arxiv.org/abs/2403.17297)
 
 </div>
 
@@ -48,25 +48,26 @@ InternLM3 supports both the deep thinking mode for solving complicated reasoning
 
 We conducted a comprehensive evaluation of InternLM using the open-source evaluation tool [OpenCompass](https://github.com/internLM/OpenCompass/). The evaluation covered five dimensions of capabilities: disciplinary competence, language competence, knowledge competence, inference competence, and comprehension competence. Here are some of the evaluation results, and you can visit the [OpenCompass leaderboard](https://rank.opencompass.org.cn) for more evaluation results.
 
-| Benchmark    |                                 | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
-| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
-| General      | CMMLU(0-shot)                   | **83.1**              | 75.8                | 53.9                 | 66.0                      |
-|              | MMLU(0-shot)                    | 76.6                  | **76.8**            | 71.8                 | 82.7                      |
-|              | MMLU-Pro(0-shot)                | **57.6**              | 56.2                | 48.1                 | 64.1                      |
-| Reasoning    | GPQA-Diamond(0-shot)            | **37.4**              | 33.3                | 24.2                 | 42.9                      |
-|              | DROP(0-shot)                    | **83.1**              | 80.4                | 81.6                 | 85.2                      |
-|              | HellaSwag(10-shot)              | **91.2**              | 85.3                | 76.7                 | 89.5                      |
-|              | KOR-Bench(0-shot)               | **56.4**              | 44.6                | 47.7                 | 58.2                      |
-| MATH         | MATH-500(0-shot)                | **83.0***             | 72.4                | 48.4                 | 74.0                      |
-|              | AIME2024(0-shot)                | **20.0***             | 16.7                | 6.7                  | 13.3                      |
-| Coding       | LiveCodeBench(2407-2409 Pass@1) | **17.8**              | 16.8                | 12.9                 | 21.8                      |
-|              | HumanEval(Pass@1)               | 82.3                  | **85.4**            | 72.0                 | 86.6                      |
-| Instrunction | IFEval(Prompt-Strict)           | **79.3**              | 71.7                | 75.2                 | 79.7                      |
-| Long Context | RULER(4-128K Average)           | 87.9                  | 81.4                | **88.5**             | 90.7                      |
-| Chat         | AlpacaEval 2.0(LC WinRate)      | **51.1**              | 30.3                | 25.0                 | 50.7                      |
-|              | WildBench(Raw Score)            | **33.1**              | 23.3                | 1.5                  | 40.3                      |
-|              | MT-Bench-101(Score 1-10)        | **8.59**              | 8.49                | 8.37                 | 8.87                      |
+|              | Benchmark                       | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(closed source) |
+| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | -------------------------- |
+| General      | CMMLU(0-shot)                   | **83.1**              | 75.8                | 53.9                 | 66.0                       |
+|              | MMLU(0-shot)                    | 76.6                  | **76.8**            | 71.8                 | 82.7                       |
+|              | MMLU-Pro(0-shot)                | **57.6**              | 56.2                | 48.1                 | 64.1                       |
+| Reasoning    | GPQA-Diamond(0-shot)            | **37.4**              | 33.3                | 24.2                 | 42.9                       |
+|              | DROP(0-shot)                    | **83.1**              | 80.4                | 81.6                 | 85.2                       |
+|              | HellaSwag(10-shot)              | **91.2**              | 85.3                | 76.7                 | 89.5                       |
+|              | KOR-Bench(0-shot)               | **56.4**              | 44.6                | 47.7                 | 58.2                       |
+| MATH         | MATH-500(0-shot)                | **83.0***             | 72.4                | 48.4                 | 74.0                       |
+|              | AIME2024(0-shot)                | **20.0***             | 16.7                | 6.7                  | 13.3                       |
+| Coding       | LiveCodeBench(2407-2409 Pass@1) | **17.8**              | 16.8                | 12.9                 | 21.8                       |
+|              | HumanEval(Pass@1)               | 82.3                  | **85.4**            | 72.0                 | 86.6                       |
+| Instrunction | IFEval(Prompt-Strict)           | **79.3**              | 71.7                | 75.2                 | 79.7                       |
+| Long Context | RULER(4-128K Average)           | 87.9                  | 81.4                | **88.5**             | 90.7                       |
+| Chat         | AlpacaEval 2.0(LC WinRate)      | **51.1**              | 30.3                | 25.0                 | 50.7                       |
+|              | WildBench(Raw Score)            | **33.1**              | 23.3                | 1.5                  | 40.3                       |
+|              | MT-Bench-101(Score 1-10)        | **8.59**              | 8.49                | 8.37                 | 8.87                       |
 
+- Values marked in bold indicate the **highest** in open source models
 - The evaluation results were obtained from [OpenCompass](https://github.com/internLM/OpenCompass/) (some data marked with *, which means evaluating with Thinking Mode), and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/internLM/OpenCompass/). 
 - The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/internLM/OpenCompass/), so please refer to the latest evaluation results of [OpenCompass](https://github.com/internLM/OpenCompass/).
 
@@ -85,8 +86,9 @@ To load the InternLM3 8B Instruct model using Transformers, use the following co
 
 ```python
 import torch
-from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
@@ -161,6 +163,7 @@ Find more details in the [LMDeploy documentation](https://lmdeploy.readthedocs.i
 ####  Ollama inference
 
 First install ollama,
+
 ```python
 # install ollama
 curl -fsSL https://ollama.com/install.sh | sh
@@ -199,17 +202,14 @@ stream = ollama.chat(
 for chunk in stream:
   print(chunk['message']['content'], end='', flush=True)
 ```
+
+
 #### vLLM inference
 
-We are still working on merging the PR(https://github.com/vllm-project/vllm/pull/12037) into vLLM. In the meantime, please use the following PR link to install it manually.
+Refer to [installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to install the latest code of vllm
 
 ```python
-git clone -b support-internlm3 https://github.com/RunningLeon/vllm.git
-# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
-cd vllm
-python use_existing_torch.py
-pip install -r requirements-build.txt
-pip install -e . --no-build-isolatio
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 ```
 
 inference code:
@@ -306,8 +306,9 @@ Focus on clear, logical progression of ideas and thorough explanation of your ma
 #### Transformers inference
 ```python
 import torch
-from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
@@ -403,14 +404,10 @@ for chunk in stream:
 
 #### vLLM inference
 
-We are still working on merging the PR(https://github.com/vllm-project/vllm/pull/12037) into vLLM. In the meantime, please use the following PR link to install it manually.
+Refer to [installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) to install the latest code of vllm
+
 ```python
-git clone https://github.com/RunningLeon/vllm.git
-# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
-cd vllm
-python use_existing_torch.py
-pip install -r requirements-build.txt
-pip install -e . --no-build-isolatio
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 ```
 
 inference code
@@ -474,25 +471,26 @@ InternLM3支持通过长思维链求解复杂推理任务的深度思考模式
 
 我们使用开源评测工具 [OpenCompass](https://github.com/internLM/OpenCompass/) 从学科综合能力、语言能力、知识能力、推理能力、理解能力五大能力维度对InternLM开展全面评测，部分评测结果如下表所示，欢迎访问[ OpenCompass 榜单 ](https://rank.opencompass.org.cn)获取更多的评测结果。
 
-| 评测集\模型  |                                 | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(close source) |
-| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ------------------------- |
-| General      | CMMLU(0-shot)                   | **83.1**              | 75.8                | 53.9                 | 66.0                      |
-|              | MMLU(0-shot)                    | 76.6                  | **76.8**            | 71.8                 | 82.7                      |
-|              | MMLU-Pro(0-shot)                | **57.6**              | 56.2                | 48.1                 | 64.1                      |
-| Reasoning    | GPQA-Diamond(0-shot)            | **37.4**              | 33.3                | 24.2                 | 42.9                      |
-|              | DROP(0-shot)                    | **83.1**              | 80.4                | 81.6                 | 85.2                      |
-|              | HellaSwag(10-shot)              | **91.2**              | 85.3                | 76.7                 | 89.5                      |
-|              | KOR-Bench(0-shot)               | **56.4**              | 44.6                | 47.7                 | 58.2                      |
-| MATH         | MATH-500(0-shot)                | **83.0***             | 72.4                | 48.4                 | 74.0                      |
-|              | AIME2024(0-shot)                | **20.0***             | 16.7                | 6.7                  | 13.3                      |
-| Coding       | LiveCodeBench(2407-2409 Pass@1) | **17.8**              | 16.8                | 12.9                 | 21.8                      |
-|              | HumanEval(Pass@1)               | 82.3                  | **85.4**            | 72.0                 | 86.6                      |
-| Instrunction | IFEval(Prompt-Strict)           | **79.3**              | 71.7                | 75.2                 | 79.7                      |
-| LongContext  | RULER(4-128K Average)           | 87.9                  | 81.4                | **88.5**             | 90.7                      |
-| Chat         | AlpacaEval 2.0(LC WinRate)      | **51.1**              | 30.3                | 25.0                 | 50.7                      |
-|              | WildBench(Raw Score)            | **33.1**              | 23.3                | 1.5                  | 40.3                      |
-|              | MT-Bench-101(Score 1-10)        | **8.59**              | 8.49                | 8.37                 | 8.87                      |
+|              | 评测集\模型                     | InternLM3-8B-Instruct | Qwen2.5-7B-Instruct | Llama3.1-8B-Instruct | GPT-4o-mini(闭源) |
+| ------------ | ------------------------------- | --------------------- | ------------------- | -------------------- | ----------------- |
+| General      | CMMLU(0-shot)                   | **83.1**              | 75.8                | 53.9                 | 66.0              |
+|              | MMLU(0-shot)                    | 76.6                  | **76.8**            | 71.8                 | 82.7              |
+|              | MMLU-Pro(0-shot)                | **57.6**              | 56.2                | 48.1                 | 64.1              |
+| Reasoning    | GPQA-Diamond(0-shot)            | **37.4**              | 33.3                | 24.2                 | 42.9              |
+|              | DROP(0-shot)                    | **83.1**              | 80.4                | 81.6                 | 85.2              |
+|              | HellaSwag(10-shot)              | **91.2**              | 85.3                | 76.7                 | 89.5              |
+|              | KOR-Bench(0-shot)               | **56.4**              | 44.6                | 47.7                 | 58.2              |
+| MATH         | MATH-500(0-shot)                | **83.0***             | 72.4                | 48.4                 | 74.0              |
+|              | AIME2024(0-shot)                | **20.0***             | 16.7                | 6.7                  | 13.3              |
+| Coding       | LiveCodeBench(2407-2409 Pass@1) | **17.8**              | 16.8                | 12.9                 | 21.8              |
+|              | HumanEval(Pass@1)               | 82.3                  | **85.4**            | 72.0                 | 86.6              |
+| Instrunction | IFEval(Prompt-Strict)           | **79.3**              | 71.7                | 75.2                 | 79.7              |
+| LongContext  | RULER(4-128K Average)           | 87.9                  | 81.4                | **88.5**             | 90.7              |
+| Chat         | AlpacaEval 2.0(LC WinRate)      | **51.1**              | 30.3                | 25.0                 | 50.7              |
+|              | WildBench(Raw Score)            | **33.1**              | 23.3                | 1.5                  | 40.3              |
+|              | MT-Bench-101(Score 1-10)        | **8.59**              | 8.49                | 8.37                 | 8.87              |
 
+- 表中标粗的数值表示在对比的开源模型中的最高值。
 - 以上评测结果基于 [OpenCompass](https://github.com/internLM/OpenCompass/) 获得(部分数据标注`*`代表使用深度思考模式进行评测)，具体测试细节可参见 [OpenCompass](https://github.com/internLM/OpenCompass/) 中提供的配置文件。
 - 评测数据会因 [OpenCompass](https://github.com/internLM/OpenCompass/) 的版本迭代而存在数值差异，请以 [OpenCompass](https://github.com/internLM/OpenCompass/) 最新版的评测结果为主。
 
@@ -515,8 +513,9 @@ transformers >= 4.48
 
 ```python
 import torch
-from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
 model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
@@ -634,17 +633,13 @@ for chunk in stream:
 
 
 #### 
+
 ##### vLLM 推理
 
-我们还在推动PR(https://github.com/vllm-project/vllm/pull/12037) 合入vllm，现在请使用以下PR链接手动安装
+参考[文档](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) 安装 vllm 最新代码
 
-```python
-git clone https://github.com/RunningLeon/vllm.git
-# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
-cd vllm
-python use_existing_torch.py
-pip install -r requirements-build.txt
-pip install -e . --no-build-isolatio
+```bash
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 ```
 
 推理代码
@@ -740,11 +735,12 @@ Focus on clear, logical progression of ideas and thorough explanation of your ma
 
 ```python
 import torch
-from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
-model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm3-8b-instruct')
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_dir = "internlm/internlm3-8b-instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
 # Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
-model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.float16).cuda()
+model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda()
 # (Optional) If on low resource devices, you can load model in 4-bit or 8-bit to further save GPU memory via bitsandbytes.
   # InternLM3 8B in 4bit will cost nearly 8GB GPU memory.
   # pip install -U bitsandbytes
@@ -837,15 +833,10 @@ for chunk in stream:
 
 ##### vLLM 推理
 
-我们还在推动PR(https://github.com/vllm-project/vllm/pull/12037) 合入vllm，现在请使用以下PR链接手动安装
+参考[文档](https://docs.vllm.ai/en/latest/getting_started/installation/index.html) 安装 vllm 最新代码
 
-```python
-git clone https://github.com/RunningLeon/vllm.git
-# and then follow  https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#build-wheel-from-source to install 
-cd vllm
-python use_existing_torch.py
-pip install -r requirements-build.txt
-pip install -e . --no-build-isolatio
+```bash
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
 ```
 
 推理代码
@@ -895,5 +886,4 @@ print(outputs)
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
-
+```

modeling_internlm3.py

@@ -793,7 +793,7 @@ class InternLM3Model(InternLM3PreTrainedModel):
     Args:
         config: InternLM3Config
     """
-
+    _auto_class = "AutoModel"
     def __init__(self, config: InternLM3Config):
         super().__init__(config)
         self.padding_idx = config.pad_token_id
@@ -1070,6 +1070,7 @@ class KwargsForCausalLM(FlashAttentionKwargs, LossKwargs): ...
 
 
 class InternLM3ForCausalLM(InternLM3PreTrainedModel, GenerationMixin):
+    _auto_class = "AutoModelForCausalLM"
     _tied_weights_keys = ["lm_head.weight"]
     _tp_plan = {"lm_head": "colwise_rep"}
 

tokenization_internlm3.py

@@ -67,7 +67,7 @@ class InternLM3Tokenizer(PreTrainedTokenizer):
             Whether or not to add an initial space to the input. This allows to treat the leading word just as any
             other word. Again, this should be set with `from_slow=True` to make sure it's taken into account.
     """
-
+    _auto_class = "AutoTokenizer"
     vocab_files_names = VOCAB_FILES_NAMES
     model_input_names = ["input_ids", "attention_mask"]