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<!-- [![Documentation Status](https://readthedocs.org/projects/internlm/badge/?version=latest)](https://internlm.readthedocs.io/zh_CN/latest/?badge=latest) -->
[📘Chat](./chat) |
[🛠️Agent](./agent) |
[📊Evaluation](./evaluation) |
[📊Evaluation](#evaluation) |
[👀Model](./model_cards) |
[🤗HuggingFace](https://huggingface.co/spaces/internlm/internlm2-Chat-7B) |
[🆕Update News](#news) |
@ -58,25 +58,69 @@ InternLM2 series are released with the following features:
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM2 Chat 20B** | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 |
| **InternLM2 20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 |
| **InternLM2 Chat 20B SFT** | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 |
| **InternLM2 Base 20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 |
| **InternLM2 Chat 7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 |
| **InternLM2 7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 |
| **InternLM2 Chat 7B SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 |
| **InternLM2 Base 7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 |
| **InternLM2-Base-7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 |
| **InternLM2-7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 |
| **InternLM2-Chat-7B-SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 |
| **InternLM2-Chat-7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 |
| **InternLM2-Base-20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 |
| **InternLM2-20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 |
| **InternLM2-Chat-20B-SFT** | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 |
| **InternLM2-Chat-20B** | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 |
**Note of Models:**
The release of InternLM2 series contains two model sizes: 7B and 20B. 7B models are efficient for research and application and 20B models are more powerful and can support more complex scenarios. For each model size, there are three types of models for different user requirements
The release of InternLM2 series contains two model sizes: 7B and 20B. 7B models are efficient for research and application and 20B models are more powerful and can support more complex scenarios. For each model size, there are four types of models for different user requirements
1. InternLM2-Base: Foundation models with high quality and high adaptation flexibility, which serves as a good starting point for downstream deep adaptations.
1. InternLM2-Base: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
2. InternLM2: Optimized in multiple dimensions based on InternLM2-Base, obtaining state-of-the-art performance in evaluation with good language capability. InternLM2 models are recommended for consideration in most applications.
3. InternLM2-Chat: InternLM2-Chat have gone through SFT and online RLHF based on InternLM2-Base model, for better instruction following, chat experience and function calling, which is recommended for downstream applications. We also released their corresponding SFT version, termed InternLM2 Chat 7/20B SFT, to ease the research for alignment.
3. InternLM2-Chat-SFT: Intermediate version of InternLM2-Chat that only undergoes supervised fine-tuning (SFT), based on the InternLM2-Base model. We release them to benefit research on alignment.
4. InternLM2-Chat: Further aligned on top of InternLM2-Chat-SFT through online RLHF. InternLM2-Chat exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
**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.
## Performance
### Objective Evaluation
| Dataset | Baichuan2-7B-Chat | Mistral-7B-Instruct-v0.2 | Qwen-7B-Chat | InternLM2-Chat-7B | ChatGLM3-6B | Baichuan2-13B-Chat | Mixtral-8x7B-Instruct-v0.1 | Qwen-14B-Chat | InternLM2-Chat-20B |
|-----------------------|-------------------|--------------------------|--------------|-------------------|-------------|---------------------|--------------------------------|---------------|---------------------|
| MMLU | 50.1 | 59.2 | 57.1 | 63.7 | 58.0 | 56.6 | 70.3 | 66.7 | 65.1 |
| CMMLU | 53.4 | 42.0 | 57.9 | 63.0 | 57.8 | 54.8 | 50.6 | 68.1 | 65.1 |
| AGIEval | 35.3 | 34.5 | 39.7 | 47.2 | 44.2 | 40.0 | 41.7 | 46.5 | 50.3 |
| C-Eval | 53.9 | 42.4 | 59.8 | 60.8 | 59.1 | 56.3 | 54.0 | 71.5 | 63.0 |
| TrivialQA | 37.6 | 35.0 | 46.1 | 50.8 | 38.1 | 40.3 | 57.7 | 54.5 | 53.9 |
| NaturalQuestions | 12.8 | 8.1 | 18.6 | 24.1 | 14.0 | 12.7 | 22.5 | 22.9 | 25.9 |
| C3 | 78.5 | 66.9 | 84.4 | 91.5 | 79.3 | 84.4 | 82.1 | 91.5 | 93.5 |
| CMRC | 8.1 | 5.6 | 14.6 | 63.8 | 43.2 | 27.8 | 5.3 | 13.0 | 50.4 |
| WinoGrande | 49.9 | 50.8 | 54.2 | 65.8 | 61.7 | 50.9 | 60.9 | 55.7 | 74.8 |
| BBH | 35.9 | 46.5 | 45.5 | 61.2 | 56.0 | 42.5 | 57.3 | 55.8 | 68.3 |
| GSM-8K | 32.4 | 48.3 | 44.1 | 70.7 | 53.8 | 56.0 | 71.7 | 57.7 | 79.6 |
| Math | 5.7 | 8.6 | 12.0 | 23.0 | 20.4 | 4.3 | 22.5 | 27.6 | 31.9 |
| HumanEval | 17.7 | 35.4 | 36.0 | 59.8 | 52.4 | 19.5 | 37.8 | 40.9 | 67.1 |
| MBPP | 37.7 | 25.7 | 33.9 | 51.4 | 55.6 | 40.9 | 40.9 | 30.0 | 65.8 |
- Performance of MBPP is reported with MBPP(Sanitized)
### Alignment Evaluation
- We have evaluated our model on [AlpacaEval 2.0](https://tatsu-lab.github.io/alpaca_eval/) and InternLM2-Chat-20B surpass Claude 2, GPT-4(0613) and Gemini Pro.
| Model Name | Win Rate | Length |
| ----------------------- | -------- | ------ |
| GPT-4 Turbo | 50.00% | 2049 |
| GPT-4 | 23.58% | 1365 |
| GPT-4 0314 | 22.07% | 1371 |
| Mistral Medium | 21.86% | 1500 |
| XwinLM 70b V0.1 | 21.81% | 1775 |
| InternLM2 Chat 20B | 21.75% | 2373 |
| Mixtral 8x7B v0.1 | 18.26% | 1465 |
| Claude 2 | 17.19% | 1069 |
| Gemini Pro | 16.85% | 1315 |
| GPT-4 0613 | 15.76% | 1140 |
| Claude 2.1 | 15.73% | 1096 |
* According to the released performance of 2024-01-17.
## Usages
We briefly show the usages with [Transformers](#import-from-transformers), [ModelScope](#import-from-modelscope), and [Web demos](#dialogue).
@ -84,30 +128,33 @@ The chat models adopt [chatml format](./chat/chat_format.md) to support both cha
### Import from Transformers
To load the InternLM2 7B Chat model using Transformers, use the following code:
To load the InternLM2-7B-Chat model using Transformers, use the following code:
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True).cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "hello", history=[])
>>> print(response)
Hello! How can I help you today?
>>> response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
>>> print(response)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", 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("internlm/internlm2-chat-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)
# Output: Hello? How can I help you today?
response, history = model.chat(tokenizer, "please provide three suggestions about time management", history=history)
print(response)
```
### Import from ModelScope
To load the InternLM model using ModelScope, use the following code:
To load the InternLM2-7B-Chat model using ModelScope, use the following code:
```python
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
import torch
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b')
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", 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, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16)
model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)
@ -125,10 +172,6 @@ pip install transformers==4.30.2
streamlit run ./chat/web_demo.py
```
The effect is similar to below:
![demo](https://github.com/InternLM/InternLM/assets/9102141/11b60ee0-47e4-42c0-8278-3051b2f17fe4)
### Deployment
We use [LMDeploy](https://github.com/InternLM/LMDeploy) for fast deployment of InternLM.
@ -154,6 +197,31 @@ Please refer to [finetune docs](./finetune/) for fine-tuning with InternLM.
**Note:** We have migrated the whole training functionality in this project to [InternEvo](https://github.com/InternLM/InternEvo) for easier user experience, which provides efficient pre-training and fine-tuning infra for training InternLM.
## Evaluation
We utilize [OpenCompass](https://github.com/open-compass/opencompass) for model evaluation. In InternLM-2, we primarily focus on standard objective evaluation, long-context evaluation (needle in a haystack), data contamination assessment, agent evaluation, and subjective evaluation.
### Objective Evaluation
To evaluate the InternLM model, please follow the guidelines in the [OpenCompass tutorial](https://opencompass.readthedocs.io/en/latest/get_started/installation.html). Typically, we use `ppl` for multiple-choice questions on the **Base** model and `gen` for all questions on the **Chat** model.
### Long-Context Evaluation (Needle in a Haystack)
For the `Needle in a Haystack` evaluation, refer to the tutorial provided in the [documentation](https://github.com/open-compass/opencompass/blob/main/docs/en/advanced_guides/needleinahaystack_eval.md). Feel free to try it out.
### Data Contamination Assessment
To learn more about data contamination assessment, please check the [contamination eval](https://opencompass.readthedocs.io/en/latest/advanced_guides/contamination_eval.html).
### Agent Evaluation
- To evaluate tool utilization, please refer to [T-Eval](https://github.com/open-compass/T-Eval).
- For code interpreter evaluation, use the [gsm-8k-agent](https://github.com/open-compass/opencompass/blob/main/configs/datasets/gsm8k/gsm8k_agent_gen_be1606.py) provided in the repository. Additionally, you need to install [Lagent](https://github.com/InternLM/lagent).
### Subjective Evaluation
- Please follow the [tutorial](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html) for subjective evaluation.
## Contribution
We appreciate all the contributors for their efforts to improve and enhance InternLM. Community users are highly encouraged to participate in the project. Please refer to the contribution guidelines for instructions on how to contribute to the project.

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[📘对话教程](./chat) |
[🛠️智能体教程](./agent) |
[📊评测](./evaluation) |
[📊评测](#评测) |
[👀模型库](./model_cards) |
[🤗HuggingFace](https://huggingface.co/spaces/internlm/internlm2-Chat-7B) |
[🆕Update News](#news) |
@ -39,14 +39,14 @@
InternLM2 系列模型在本仓库正式发布，具有如下特性：
- 有效支持20万字超长上下文：模型在20万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。 可以通过 [LMDeploy](./inference/) 尝试20万字超长上下文推理。
- 有效支持20万字超长上下文：模型在 20 万字长输入中几乎完美地实现长文“大海捞针”，而且在 LongBench 和 L-Eval 等长文任务中的表现也达到开源模型中的领先水平。 可以通过 [LMDeploy](./inference/) 尝试20万字超长上下文推理。
- 综合性能全面提升：各能力维度相比上一代模型全面进步，在推理、数学、代码、对话体验、指令遵循和创意写作等方面的能力提升尤为显著，综合性能达到同量级开源模型的领先水平，在重点能力评测上 InternLM2-Chat-20B 能比肩甚至超越 ChatGPT （GPT-3.5）。
- 代码解释器与数据分析：在配合代码解释器（code-interpreter）的条件下，InternLM2-Chat-20B 在 GSM8K 和 MATH 上可以达到和 GPT-4 相仿的水平。基于在数理和工具方面强大的基础能力，InternLM2-Chat 提供了实用的数据分析能力。
- 工具调用能力整体升级：基于更强和更具有泛化性的指令理解、工具筛选与结果反思等能力，新版模型可以更可靠地支持复杂智能体的搭建，支持对工具进行有效的多轮调用，完成较复杂的任务。可以查看更多[样例](./agent/)。
## 更新
[2024.01.17] 我们发布了 InternLM2-7B 和 InternLM2-20B 以及相关的对话模型，InternLM2 在数理、代码、对话、创作等各方面能力都获得了长足进步，综合性能达到开源模型的领先水平。可以点击 [下面的模型库](#model-zoo)进行下载或者[查看模型文档](./model_cards/)来了解更多细节.
[2024.01.17] 我们发布了 InternLM2-7B 和 InternLM2-20B 以及相关的对话模型，InternLM2 在数理、代码、对话、创作等各方面能力都获得了长足进步，综合性能达到开源模型的领先水平。可以点击[下面的模型库](#model-zoo)进行下载或者[查看模型文档](./model_cards/)来了解更多细节.
[2023.12.13] 我们更新了 InternLM-7B-Chat 和 InternLM-20B-Chat 模型权重。通过改进微调数据和训练策略，新版对话模型生成的回复质量更高、语言风格更加多元。
@ -56,44 +56,89 @@ InternLM2 系列模型在本仓库正式发布，具有如下特性：
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM2 Chat 20B** | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 |
| **InternLM2 20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 |
| **InternLM2 Chat 20B SFT** | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 |
| **InternLM2 Base 20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 |
| **InternLM2 Chat 7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 |
| **InternLM2 7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 |
| **InternLM2 Chat 7B SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 |
| **InternLM2 Base 7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 |
| **InternLM2-Base-7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 |
| **InternLM2-7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 |
| **InternLM2-Chat-7B-SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 |
| **InternLM2-Chat-7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 |
| **InternLM2-Base-20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 |
| **InternLM2-20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 |
| **InternLM2-Chat-20B-SFT** | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 |
| **InternLM2-Chat-20B** | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [<img src="./assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 |
**关于模型说明：**
在此次发布中，InternLM2 包含两种模型规格：7B和20B。7B为轻量级的研究和应用提供了一个轻便但性能不俗的模型，20B模型的综合性能更为强劲，可以有效支持更加复杂的实用场景。面向不同的使用需求，每个规格包含三个模型版本：
在此次发布中，InternLM2 包含两种模型规格：7B 和 20B。7B 为轻量级的研究和应用提供了一个轻便但性能不俗的模型，20B 模型的综合性能更为强劲，可以有效支持更加复杂的实用场景。面向不同的使用需求，每个规格包含四个模型版本：
1. InternLM2-Base：高质量和具有很强可塑性的模型基座，是模型进行深度领域适配的高质量起点。
2. InternLM2：在Base基础上，在多个能力方向进行了强化，在评测中成绩优异，同时保持了很好的通用语言能力，是我们推荐的在大部分应用中考虑选用的优秀基座。
3. InternLM2-Chat：InternLM2-Chat 模型在 InternLM2-Base 模型的基础上，经过了 SFT 和 RLHF，面向对话交互进行了优化，具有较好的指令遵循、共情聊天和调用工具等的能力，是我们推荐直接用于下游应用的模型。我们同时开源了这些模型使用的 SFT 版本方便社区的对齐研究，标记为 InternLM2-Chat 7B/20B SFT。
2. InternLM2：在 Base 模型基础上，在多个能力方向进行了强化，在评测中成绩优异，同时保持了很好的通用语言能力，是我们推荐的在大部分应用中考虑选用的优秀基座。
3. InternLM2-Chat-SFT: 基于 InternLM2-Base 模型进行了有监督微调，是 InternLM2-Chat 模型的中间版本。我们将它们开源以助力社区在对齐方面的研究。
4. InternLM2-Chat: 在 InternLM2-Chat-SFT 的基础上进行了 online RLHF 以进一步对齐. InternLM2-Chat 面向对话交互进行了优化，具有较好的指令遵循、共情聊天和调用工具等的能力，是我们推荐直接用于下游应用的模型。
**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。
## 性能
### 客观评测
| Dataset | Baichuan2-7B-Chat | Mistral-7B-Instruct-v0.2 | Qwen-7B-Chat | InternLM2-Chat-7B | ChatGLM3-6B | Baichuan2-13B-Chat | Mixtral-8x7B-Instruct-v0.1 | Qwen-14B-Chat | InternLM2-Chat-20B |
|-----------------------|-------------------|--------------------------|--------------|-------------------|-------------|---------------------|--------------------------------|---------------|---------------------|
| MMLU | 50.1 | 59.2 | 57.1 | 63.7 | 58.0 | 56.6 | 70.3 | 66.7 | 65.1 |
| CMMLU | 53.4 | 42.0 | 57.9 | 63.0 | 57.8 | 54.8 | 50.6 | 68.1 | 65.1 |
| AGIEval | 35.3 | 34.5 | 39.7 | 47.2 | 44.2 | 40.0 | 41.7 | 46.5 | 50.3 |
| C-Eval | 53.9 | 42.4 | 59.8 | 60.8 | 59.1 | 56.3 | 54.0 | 71.5 | 63.0 |
| TrivialQA | 37.6 | 35.0 | 46.1 | 50.8 | 38.1 | 40.3 | 57.7 | 54.5 | 53.9 |
| NaturalQuestions | 12.8 | 8.1 | 18.6 | 24.1 | 14.0 | 12.7 | 22.5 | 22.9 | 25.9 |
| C3 | 78.5 | 66.9 | 84.4 | 91.5 | 79.3 | 84.4 | 82.1 | 91.5 | 93.5 |
| CMRC | 8.1 | 5.6 | 14.6 | 63.8 | 43.2 | 27.8 | 5.3 | 13.0 | 50.4 |
| WinoGrande | 49.9 | 50.8 | 54.2 | 65.8 | 61.7 | 50.9 | 60.9 | 55.7 | 74.8 |
| BBH | 35.9 | 46.5 | 45.5 | 61.2 | 56.0 | 42.5 | 57.3 | 55.8 | 68.3 |
| GSM-8K | 32.4 | 48.3 | 44.1 | 70.7 | 53.8 | 56.0 | 71.7 | 57.7 | 79.6 |
| Math | 5.7 | 8.6 | 12.0 | 23.0 | 20.4 | 4.3 | 22.5 | 27.6 | 31.9 |
| HumanEval | 17.7 | 35.4 | 36.0 | 59.8 | 52.4 | 19.5 | 37.8 | 40.9 | 67.1 |
| MBPP | 37.7 | 25.7 | 33.9 | 51.4 | 55.6 | 40.9 | 40.9 | 30.0 | 65.8 |
- MBPP性能使用的是MBPP(Sanitized)版本数据集
### 主观评测
- 我们评测了InternLM2-Chat在[AlpacaEval 2.0](https://tatsu-lab.github.io/alpaca_eval/) 上的性能，结果表明InternLM2-Chat在AlpacaEval上已经超过了 Claude 2, GPT-4(0613) 和 Gemini Pro.
| Model Name | Win Rate | Length |
| ----------------------- | -------- | ------ |
| GPT-4 Turbo | 50.00% | 2049 |
| GPT-4 | 23.58% | 1365 |
| GPT-4 0314 | 22.07% | 1371 |
| Mistral Medium | 21.86% | 1500 |
| XwinLM 70b V0.1 | 21.81% | 1775 |
| InternLM2 Chat 20B | 21.75% | 2373 |
| Mixtral 8x7B v0.1 | 18.26% | 1465 |
| Claude 2 | 17.19% | 1069 |
| Gemini Pro | 16.85% | 1315 |
| GPT-4 0613 | 15.76% | 1140 |
| Claude 2.1 | 15.73% | 1096 |
* 性能数据截止2024-01-17
## 使用案例
接下来我们展示使用 [Transformers](#import-from-transformers), [ModelScope](#import-from-modelscope), 和 [Web demo](#dialogue) 进行推理.
接下来我们展示使用 [Transformers](#import-from-transformers)，[ModelScope](#import-from-modelscope) 和 [Web demo](#dialogue) 进行推理。
对话模型采用了 [chatml 格式](./chat/chat_format.md) 来支持通用对话和智能体应用。
### 通过 Transformers 加载
通过以下的代码从 Transformers 加载 InternLM 模型 （可修改模型名称替换不同的模型）
通过以下的代码从 Transformers 加载 InternLM2-7B-Chat 模型 （可修改模型名称替换不同的模型）
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True).cuda()
>>> model = model.eval()
>>> response, history = model.chat(tokenizer, "你好", history=[])
>>> print(response)
你好！有什么我可以帮助你的吗？
>>> response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history)
>>> print(response)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True)
# 设置`torch_dtype=torch.float16`来将模型精度指定为torch.float16，否则可能会因为您的硬件原因造成显存不足的问题。
model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True, torch_dtype=torch.float16).cuda()
model = model.eval()
response, history = model.chat(tokenizer, "你好", history=[])
print(response)
# 模型输出：你好！有什么我可以帮助你的吗？
response, history = model.chat(tokenizer, "请提供三个管理时间的建议。", history=history)
print(response)
```
### 通过 ModelScope 加载
@ -101,11 +146,11 @@ InternLM2 系列模型在本仓库正式发布，具有如下特性：
通过以下的代码从 ModelScope 加载 InternLM 模型 （可修改模型名称替换不同的模型）
```python
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
import torch
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm2-chat-7b')
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_dir,device_map="auto", trust_remote_code=True,torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_dir, device_map="auto", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_dir, device_map="auto", trust_remote_code=True, torch_dtype=torch.float16)
model = model.eval()
response, history = model.chat(tokenizer, "hello", history=[])
print(response)
@ -123,11 +168,7 @@ pip install transformers==4.30.2
streamlit run ./chat/web_demo.py
```
效果如下
![效果](https://github.com/InternLM/InternLM/assets/9102141/11b60ee0-47e4-42c0-8278-3051b2f17fe4)
### 基于InternLM高性能部署
### 基于 InternLM 高性能部署
我们使用 [LMDeploy](https://github.com/InternLM/LMDeploy) 完成 InternLM 的一键部署。
@ -146,7 +187,32 @@ print(response)
请参考[微调教程](./finetune/)尝试续训或微调 InternLM2。
**注意：**本项目中的全量训练功能已经迁移到了[InternEvo](https://github.com/InternLM/InternEvo)以便捷用户的使用。InternEvo 提供了高效的预训练和微调基建用于训练 InternLM 系列模型。
**注意：** 本项目中的全量训练功能已经迁移到了 [InternEvo](https://github.com/InternLM/InternEvo) 以便用户使用。InternEvo 提供了高效的预训练和微调基建用于训练 InternLM 系列模型。
## 评测
我们使用 [OpenCompass](https://github.com/open-compass/opencompass) 进行模型评估。在 InternLM-2 中，我们主要标准客观评估、长文评估（大海捞针）、数据污染评估、智能体评估和主观评估。
### 标准客观评测
请按照 [OpenCompass 教程](https://opencompass.readthedocs.io/zh-cn/latest/get_started/installation.html) 进行客观评测。我们通常在 Base 模型上使用 ppl 进行多项选择题评测，在 Chat 模型上使用 gen 进行所有问题的答案生成和评测。
### 长文评估（大海捞针）
有关 `大海捞针` 评估的教程，请参阅 [文档](https://github.com/open-compass/opencompass/blob/main/docs/en/advanced_guides/needleinahaystack_eval.md) 中的教程。
### 数据污染评估
要了解更多关于数据污染评估的信息，请查看 [污染评估](https://opencompass.readthedocs.io/en/latest/advanced_guides/contamination_eval.html)。
### 智能体评估
- 要评估大模型的工具利用能力，请使用 [T-Eval](https://github.com/open-compass/T-Eval) 进行评测。
- 对于代码解释器评估，请使用 [gsm-8k-agent](https://github.com/open-compass/opencompass/blob/main/configs/datasets/gsm8k/gsm8k_agent_gen_be1606.py) 提供的配置进行评估。此外，您还需要安装 [Lagent](https://github.com/InternLM/lagent)。
### 主观评估
- 请按照 [教程](https://opencompass.readthedocs.io/en/latest/advanced_guides/subjective_evaluation.html) 进行主观评估。
## 贡献
@ -154,7 +220,7 @@ print(response)
## 致谢
InternLM 代码库是一款由上海人工智能实验室和来自不同高校、企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活高效的代码工具，供用户微调 InternLM 并开发自己的新模型，从而不断为开源社区提供贡献。特别鸣谢[flash-attention](https://github.com/HazyResearch/flash-attention) 与 [ColossalAI](https://github.com/hpcaitech/ColossalAI) 两项开源项目。
InternLM 代码库是一款由上海人工智能实验室和来自不同高校、企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供新功能支持的贡献者，以及提供宝贵反馈意见的用户。我们希望这个工具箱和基准测试可以为社区提供灵活高效的代码工具，供用户微调 InternLM 并开发自己的新模型，从而不断为开源社区提供贡献。特别鸣谢 [flash-attention](https://github.com/HazyResearch/flash-attention) 与 [ColossalAI](https://github.com/hpcaitech/ColossalAI) 两项开源项目。
## 开源许可证

109
chat/chat_format.md Normal file
View File

@ -0,0 +1,109 @@
# Chat Format
English | [简体中文](chat_format_zh-CN.md)
InternLM2-Chat adopts a new chat format to flexibly support a wider range of applications, such as tool invocation, while avoiding user input attacks. This new format is similar to the [ChatML](https://github.com/openai/openai-python/blob/release-v0.28.0/chatml.md) format, but with an added `environment` role to support general-purpose AI applications, in addition to `system`, `user`, and `assistant`.
## Basic Structure
The regular chat structure usually contains three roles: `system`, `user`, and `assistant`, formatted as follows for multi-turn dialogues:
```
[UNUSED_TOKEN_146]system
You are InternLM2-Chat, a harmless AI assistant[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user
Hello[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
Hello, I am InternLM2-Chat, how can I assist you?[UNUSED_TOKEN_145]
```
Here, `[UNUSED_TOKEN_146]` acts as the start token for each turn of dialogue, and `[UNUSED_TOKEN_145]` as the end token. Each turn of dialogue typically starts with `[UNUSED_TOKEN_146]role` and ends with the model's output `[UNUSED_TOKEN_145]`, where role represents `system`, `user`, `assistant`, and `environment`. Currently, the InternLM2-Chat model's vocabulary also maintains the following mappings:
- `[UNUSED_TOKEN_146]`: Start token for each role's dialogue
- `[UNUSED_TOKEN_145]`: End token for each role's dialogue
- `[UNUSED_TOKEN_144]`: Start token for invoking external plugins
- `[UNUSED_TOKEN_143]`: End token for invoking external plugins
- `[UNUSED_TOKEN_142]`: Code interpreter
- `[UNUSED_TOKEN_141]`: External plugins, regular tools
## Complete Structure
The complete dialogue format of InternLM2-Chat, based on the basic structure, also includes designs for general-purpose AI agents. Its core purpose is to use a streaming format that allows the same format to support various types of plugin extensions and AI environments while being compatible with general dialogue. The state of a general AI agent dialogue is shown below:
```
[UNUSED_TOKEN_146]system
You are InternLM2-Chat, a harmless AI assistant[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_142]
You now have access to a Jupyter notebook environment supporting Python code execution. Just send code to python to run in this stateful environment. This feature is suitable for:
- Data analysis or processing (such as data manipulation and graphic creation)
- Complex calculations (such as math and physics problems)
- Programming examples (for understanding programming concepts or language features)
- Text processing and analysis (including text analysis and natural language processing)
- Machine learning and data science (model training and data visualization)
- File operations and data import (handling CSV, JSON, etc. formats)
[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_141]
[
{
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"unit": {"type": "string"},
},
"required": ["location"],
},
}
]
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user
Please help me process and visualize this dataset.
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user name=file
[{"path": "data.csv", size='10K'}][UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
I have processed the data and visualized it for you.
[UNUSED_TOKEN_144][UNUSED_TOKEN_142]
```python
import plotly.express as px
import pandas as pd
# Load the data into a pandas dataframe
df = pd.read_csv('data.csv')
# Create a scatter plot of rainfall vs wind direction
fig = px.scatter(df, x='WindDir9am', y='Rainfall', color='WindDir3pm',
labels={'WindDir9am': 'Wind Direction 9am', 'Rainfall': '
Rainfall', 'WindDir3pm': 'Wind Direction 3pm'},
title='Rainfall vs Wind Direction',
template='plotly_dark',
width=600, height=500)
# Add a hover effect to show the date
fig.update_traces(hovertemplate='<b>Date: %{text}</b><br>Wind Direction 9am: %{x}<br>Rainfall: %{y}<br>Wind Direction 3pm: %{marker.color}')
# Show the plot
fig.show()
```[UNUSED_TOKEN_143]
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]environment name=[UNUSED_TOKEN_142]
![image](xxx.png)
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
Since the code output is not included here, I cannot provide specific chart content. However, if the code executed correctly, it should display a polar plot with two filled areas representing the relationship between wind direction at 9 am and rainfall, and between wind direction at 3 pm and rainfall, respectively. The values for each direction are based on the average rainfall calculated from the provided dataset. The chart should have a clear title, a legend, and be intuitive for comparing rainfall with different wind directions. Given the use of a dark theme, the overall appearance of the chart should be bright lines and filled areas on a dark background.[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user
Can you explain the code?[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
The code uses Python's Plotly library to create a polar plot that shows the relationship between wind direction and rainfall. It starts by creating a Plotly Figure object. Then, I added two `Scatterpolar` traces. The first trace shows the relationship between wind direction at 9 am and average rainfall, and the second trace shows the same for 3 pm. In these traces, the `r` parameter represents the average rainfall, and `theta` represents the wind direction. `fill='toself'` means the shape is filled to its own boundary. Next, I updated the chart layout, hid the radial axis, added a legend, set the title and its font, chose a dark theme, and set the chart's background color, width, and height. Finally, the chart was displayed using `fig.show()`.
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user
I want to know today's weather in Shanghai[UNUSED_TOKEN_145]
[UNUSED_TOKEN_144][UNUSED_TOKEN_141]
{"name": "get_current_weather", "parameters": {"location": "Shanghai"}}[UNUSED_TOKEN_143]
```

11
chat/chat_format_zh-CN.md
View File

@ -33,6 +33,14 @@ InternLM2-Chat 的完整对话格式在上述基本结构的基础上还包含
```
[UNUSED_TOKEN_146]system
你是书生浦语2，一个无害的人工智能助手[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_142]
你现在可以使用一个支持 Python 代码执行的 Jupyter 笔记本环境。只需向 python 发送代码，即可在这个有状态环境中进行运行。这个功能适用于:
- 数据分析或处理（如数据操作和图形制作）
- 复杂计算（如数学和物理问题）
- 编程示例（用于理解编程概念或语言特性）
- 文本处理和分析（包括文本分析和自然语言处理）
- 机器学习和数据科学（模型训练和数据可视化展示）
- 文件操作和数据导入（处理CSV、JSON等格式文件）
[UNUSED_TOKEN_146]system name=[UNUSED_TOKEN_141]
[
{
@ -56,7 +64,7 @@ InternLM2-Chat 的完整对话格式在上述基本结构的基础上还包含
请帮我对该数据集进行数据处理并可视化。
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]user name=file
{"path": "data.csv"}[UNUSED_TOKEN_145]
[{"path": "data.csv", size='10K'}][UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
我已经帮您处理了数据并进行了可视化。
@ -83,6 +91,7 @@ fig.show()
```[UNUSED_TOKEN_143]
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]environment name=[UNUSED_TOKEN_142]
![image](xxx.png)
[UNUSED_TOKEN_145]
[UNUSED_TOKEN_146]assistant
由于代码输出没有包含在这里，我无法提供具体的图表内容。但是，如果代码执行无误，它会显示一个极坐标图，其中包含两个填充区域，一个代表上午9点的风向与降雨量的关系，另一个代表下午3点的风向与降雨量的关系。每个方向上的值是根据提供的数据集计算得到的平均降雨量。图表应该有一个清晰的标题、图例，并且能够直观地比较不同风向下的降雨量。由于使用了暗色主题，图表的整体外观应该是深色背景上的亮色线条和填充区域。[UNUSED_TOKEN_145]

13
chat/web_demo.py
View File

@ -11,11 +11,10 @@ from dataclasses import asdict
import streamlit as st
import torch
from tools.transformers.interface import GenerationConfig, generate_interactive
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.utils import logging
from tools.transformers.interface import GenerationConfig, generate_interactive
logger = logging.get_logger(__name__)
@ -109,9 +108,15 @@ def main():
):
# Display robot response in chat message container
message_placeholder.markdown(cur_response + "▌")
message_placeholder.markdown(cur_response)
message_placeholder.markdown(cur_response) # pylint: disable=undefined-loop-variable
# Add robot response to chat history
st.session_state.messages.append({"role": "robot", "content": cur_response, "avatar": robot_avator})
st.session_state.messages.append(
{
"role": "robot",
"content": cur_response, # pylint: disable=undefined-loop-variable
"avatar": robot_avator,
}
)
torch.cuda.empty_cache()

93
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View File

@ -1,6 +1,97 @@
# Fine-tuning with InternLM
English | [简体中文](./README_zh-CN.md)
We recommend two projects to fine-tune InternLM.
1. [Xtuner](): brief introduction
1. [XTuner](https://github.com/InternLM/xtuner) is an efficient, flexible and full-featured toolkit for fine-tuning large models.
2. [InternLM-Train](): brief introduction
## XTuner
### Highlights
1. Support LLM, VLM pre-training / fine-tuning on almost all GPUs. XTuner is capable of fine-tuning InternLM2-7B on a single 8GB GPU, as well as multi-node fine-tuning of models exceeding 70B.
2. Support various training algorithms ([QLoRA](http://arxiv.org/abs/2305.14314), [LoRA](http://arxiv.org/abs/2106.09685), full-parameter fune-tune), allowing users to choose the most suitable solution for their requirements.
3. Compatible with [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀, easily utilizing a variety of ZeRO optimization techniques.
4. The output models can seamlessly integrate with deployment and server toolkit ([LMDeploy](https://github.com/InternLM/lmdeploy)), and large-scale evaluation toolkit ([OpenCompass](https://github.com/open-compass/opencompass), [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)).
### Installation
- It is recommended to build a Python 3.10 virtual environment using conda
```bash
conda create --name xtuner-env python=3.10 -y
conda activate xtuner-env
```
- Install XTuner with DeepSpeed integration
```shell
pip install -U 'xtuner[deepspeed]'
```
### Fine-tune
XTuner supports the efficient fine-tune (*e.g.*, QLoRA) for InternLM2.
- **Step 0**, prepare the config. XTuner provides many ready-to-use configs and we can view all configs of InternLM2 by
```shell
xtuner list-cfg -p internlm2
```
Or, if the provided configs cannot meet the requirements, please copy the provided config to the specified directory and make specific modifications by
```shell
xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}
vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
```
- **Step 1**, start fine-tuning.
```shell
xtuner train ${CONFIG_NAME_OR_PATH}
```
For example, we can start the QLoRA fine-tuning of InternLM2-Chat-7B with oasst1 dataset by
```shell
# On a single GPU
xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
# On multiple GPUs
(DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
(SLURM) srun ${SRUN_ARGS} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
```
- `--deepspeed` means using [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 to optimize the training. XTuner comes with several integrated strategies including ZeRO-1, ZeRO-2, and ZeRO-3. If you wish to disable this feature, simply remove this argument.
- **Step 2**, convert the saved PTH model (if using DeepSpeed, it will be a directory) to HuggingFace model, by
```shell
xtuner convert pth_to_hf ${CONFIG_NAME_OR_PATH} ${PTH} ${SAVE_PATH}
```
### Chat
XTuner provides tools to chat with pretrained / fine-tuned large models.
```shell
xtuner chat ${NAME_OR_PATH_TO_LLM} [optional arguments]
```
For example, we can start the chat with
InternLM2-Chat-7B with adapter trained from oasst1:
```shell
xtuner chat internlm/internlm2-chat-7b --adapter xtuner/internlm2-chat-7b-qlora-oasst1 --prompt-template internlm2_chat
```
LLaVA-InternLM2-7B:
```shell
xtuner chat internlm/internlm2-chat-7b --visual-encoder openai/clip-vit-large-patch14-336 --llava xtuner/llava-internlm2-7b --prompt-template internlm2_chat --image $IMAGE_PATH
```

96
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View File

@ -0,0 +1,96 @@
# 微调 InternLM
[English](./README.md) | 简体中文
我们推荐以下两种框架微调 InternLM
1. [XTuner](https://github.com/InternLM/xtuner) 是一个高效、灵活、全能的轻量化大模型微调工具库。
2. [InternLM-Train](): brief introduction
## XTuner
### 亮点
1. 支持大语言模型 LLM、多模态图文模型 VLM 的预训练及轻量级微调。XTuner 支持在 8GB 显存下微调 7B 模型，同时也支持多节点跨设备微调更大尺度模型（70B+）。
2. 支持 [QLoRA](http://arxiv.org/abs/2305.14314)、[LoRA](http://arxiv.org/abs/2106.09685)、全量参数微调等多种微调算法，支撑用户根据具体需求作出最优选择。
3. 兼容 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀，轻松应用各种 ZeRO 训练优化策略。
4. 训练所得模型可无缝接入部署工具库 [LMDeploy](https://github.com/InternLM/lmdeploy)、大规模评测工具库 [OpenCompass](https://github.com/open-compass/opencompass) 及 [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)。
### 安装
- 借助 conda 准备虚拟环境
```bash
conda create --name xtuner-env python=3.10 -y
conda activate xtuner-env
```
- 安装集成 DeepSpeed 版本的 XTuner
```shell
pip install -U 'xtuner[deepspeed]'
```
### 微调
- **步骤 0**，准备配置文件。XTuner 提供多个开箱即用的配置文件，用户可以通过下列命令查看所有 InternLM2 的预置配置文件：
```shell
xtuner list-cfg -p internlm2
```
或者，如果所提供的配置文件不能满足使用需求，请导出所提供的配置文件并进行相应更改：
```shell
xtuner copy-cfg ${CONFIG_NAME} ${SAVE_PATH}
vi ${SAVE_PATH}/${CONFIG_NAME}_copy.py
```
- **步骤 1**，开始微调。
```shell
xtuner train ${CONFIG_NAME_OR_PATH}
```
例如，我们可以利用 QLoRA 算法在 oasst1 数据集上微调 InternLM2-Chat-7B：
```shell
# 单卡
xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
# 多卡
(DIST) NPROC_PER_NODE=${GPU_NUM} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --deepspeed deepspeed_zero2
(SLURM) srun ${SRUN_ARGS} xtuner train internlm2_chat_7b_qlora_oasst1_e3 --launcher slurm --deepspeed deepspeed_zero2
```
- `--deepspeed` 表示使用 [DeepSpeed](https://github.com/microsoft/DeepSpeed) 🚀 来优化训练过程。XTuner 内置了多种策略，包括 ZeRO-1、ZeRO-2、ZeRO-3 等。如果用户期望关闭此功能，请直接移除此参数。
- **步骤 2**，将保存的 PTH 模型（如果使用的DeepSpeed，则将会是一个文件夹）转换为 HuggingFace 模型：
```shell
xtuner convert pth_to_hf ${CONFIG_NAME_OR_PATH} ${PTH} ${SAVE_PATH}
```
### 对话
XTuner 提供与大模型对话的工具。
```shell
xtuner chat ${NAME_OR_PATH_TO_LLM} [optional arguments]
```
例如：
与 InternLM2-Chat-7B, oasst1 adapter 对话：
```shell
xtuner chat internlm/internlm2-chat-7b --adapter xtuner/internlm2-chat-7b-qlora-oasst1 --prompt-template internlm2_chat
```
与 LLaVA-InternLM2-7B 对话：
```shell
xtuner chat internlm/internlm2-chat-7b --visual-encoder openai/clip-vit-large-patch14-336 --llava xtuner/llava-internlm2-7b --prompt-template internlm2_chat --image $IMAGE_PATH
```

42
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View File

@ -0,0 +1,42 @@
# InternLM2-20B Model Card
## Introduction
The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For the convenience of users and researchers, we have open-sourced four versions of each scale of the model, which are:
- internlm2-base-20b: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
- internlm2-20b (**recommended**): Optimized in multiple dimensions based on InternLM2-Base, obtaining state-of-the-art performance in evaluation with good language capability. InternLM2 models are recommended for consideration in most applications.
- internlm2-chat-20b-sft: Intermediate version of InternLM2-Chat that only undergoes supervised fine-tuning (SFT), based on the InternLM2-Base model. We release them to benefit research on alignment.
- internlm2-chat-20b (**recommended**): Further aligned on top of InternLM2-Chat-SFT through online RLHF. InternLM2-Chat exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
The base model of InternLM2 has the following technical features:
- Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
- Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
## Model Zoo
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM2-Base-20B** | [🤗internlm/internlm2-base-20b](https://huggingface.co/internlm/internlm2-base-20b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-base-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-20b) | 2024-01-17 |
| **InternLM2-20B** | [🤗internlm/internlm2-20b](https://huggingface.co/internlm/internlm2-20b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-20b) | 2024-01-17 |
| **InternLM2-Chat-20B-SFT** | [🤗internlm/internlm2-chat-20b-sft](https://huggingface.co/internlm/internlm2-chat-20b-sft) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b-sft) | 2024-01-17 |
| **InternLM2-Chat-20B** | [🤗internlm/internlm2-chat-20b](https://huggingface.co/internlm/internlm2-chat-20b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-20b) | 2024-01-17 |
## Performance Evaluation
We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass). Some of the evaluation results are shown in the table below. You are welcome to visit the [OpenCompass Leaderboard](https://opencompass.org.cn/rank) for more evaluation results.
| Dataset\Models | InternLM2-7B | InternLM2-Chat-7B | InternLM2-20B | InternLM2-Chat-20B | ChatGPT | GPT-4 |
| --- | --- | --- | --- | --- | --- | --- |
| MMLU | 65.8 | 63.7 | 67.7 | 66.5 | 69.1 | 83.0 |
| AGIEval | 49.9 | 47.2 | 53.0 | 50.3 | 39.9 | 55.1 |
| BBH | 65.0 | 61.2 | 72.1 | 68.3 | 70.1 | 86.7 |
| GSM8K | 70.8 | 70.7 | 76.1 | 79.6 | 78.2 | 91.4 |
| MATH | 20.2 | 23.0 | 25.5 | 31.9 | 28.0 | 45.8 |
| HumanEval | 43.3 | 59.8 | 48.8 | 67.1 | 73.2 | 74.4 |
| MBPP(Sanitized) | 51.8 | 51.4 | 63.0 | 65.8 | 78.9 | 79.0 |
- The evaluation results were obtained from [OpenCompass](https://github.com/open-compass/opencompass) , and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/open-compass/opencompass).
- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/open-compass/opencompass), so please refer to the latest evaluation results of [OpenCompass](https://github.com/open-compass/opencompass).

42
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@ -0,0 +1,42 @@
# InternLM2-7B Model Card
## Introduction
The second generation of the InternLM model, InternLM2, includes models at two scales: 7B and 20B. For the convenience of users and researchers, we have open-sourced four versions of each scale of the model, which are:
- internlm2-base-7b: Foundation models with high quality and high adaptation flexibility, which serve as a good starting point for downstream deep adaptations.
- internlm2-7b (**recommended**): Optimized in multiple dimensions based on InternLM2-Base, obtaining state-of-the-art performance in evaluation with good language capability. InternLM2 models are recommended for consideration in most applications.
- internlm2-chat-7b-sft: Intermediate version of InternLM2-Chat that only undergoes supervised fine-tuning (SFT), based on the InternLM2-Base model. We release them to benefit research on alignment.
- internlm2-chat-7b (**recommended**): Further aligned on top of InternLM2-Chat-SFT through online RLHF. InternLM2-Chat exhibits better instruction following, chat experience, and function calling, which is recommended for downstream applications.
The base model of InternLM2 has the following technical features:
- Effective support for ultra-long contexts of up to 200,000 characters: The model nearly perfectly achieves "finding a needle in a haystack" in long inputs of 200,000 characters. It also leads among open-source models in performance on long-text tasks such as LongBench and L-Eval.
- Comprehensive performance enhancement: Compared to the previous generation model, it shows significant improvements in various capabilities, including reasoning, mathematics, and coding.
## Model Zoo
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM2-Base-7B** | [🤗internlm/internlm2-base-7b](https://huggingface.co/internlm/internlm2-base-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-base-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-base-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-base-7b) | 2024-01-17 |
| **InternLM2-7B** | [🤗internlm/internlm2-7b](https://huggingface.co/internlm/internlm2-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-7b) | 2024-01-17 |
| **InternLM2-Chat-7B-SFT** | [🤗internlm/internlm2-chat-7b-sft](https://huggingface.co/internlm/internlm2-chat-7b-sft) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b-sft](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b-sft/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b-sft) | 2024-01-17 |
| **InternLM2-Chat-7B** | [🤗internlm/internlm2-chat-7b](https://huggingface.co/internlm/internlm2-chat-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> internlm2-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm2-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/internlm2-chat-7b) | 2024-01-17 |
## Performance Evaluation
We have evaluated InternLM2 on several important benchmarks using the open-source evaluation tool [OpenCompass](https://github.com/open-compass/opencompass). Some of the evaluation results are shown in the table below. You are welcome to visit the [OpenCompass Leaderboard](https://opencompass.org.cn/rank) for more evaluation results.
| Dataset\Models | InternLM2-7B | InternLM2-Chat-7B | InternLM2-20B | InternLM2-Chat-20B | ChatGPT | GPT-4 |
| --- | --- | --- | --- | --- | --- | --- |
| MMLU | 65.8 | 63.7 | 67.7 | 66.5 | 69.1 | 83.0 |
| AGIEval | 49.9 | 47.2 | 53.0 | 50.3 | 39.9 | 55.1 |
| BBH | 65.0 | 61.2 | 72.1 | 68.3 | 70.1 | 86.7 |
| GSM8K | 70.8 | 70.7 | 76.1 | 79.6 | 78.2 | 91.4 |
| MATH | 20.2 | 23.0 | 25.5 | 31.9 | 28.0 | 45.8 |
| HumanEval | 43.3 | 59.8 | 48.8 | 67.1 | 73.2 | 74.4 |
| MBPP(Sanitized) | 51.8 | 51.4 | 63.0 | 65.8 | 78.9 | 79.0 |
- The evaluation results were obtained from [OpenCompass](https://github.com/open-compass/opencompass) , and evaluation configuration can be found in the configuration files provided by [OpenCompass](https://github.com/open-compass/opencompass).
- The evaluation data may have numerical differences due to the version iteration of [OpenCompass](https://github.com/open-compass/opencompass), so please refer to the latest evaluation results of [OpenCompass](https://github.com/open-compass/opencompass).

4
model_cards/internlm_20b.md
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@ -15,8 +15,8 @@ In terms of model structure, InternLM-20B opted for a deeper architecture, with
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | OpenXLab(Original) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM Chat 20B** | [🤗internlm/internlm-chat-20b](https://huggingface.co/internlm/internlm-20b-chat) | [<img src="./docs/imgs/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-20b-chat/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-20b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-20b-original) | 2023-12-12 |
| **InternLM 20B** | [🤗internlm/internlm-20b](https://huggingface.co/internlm/internlm-20b) | [<img src="./docs/imgs/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-20b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-20b-original) | 2023-09-20 |
| **InternLM Chat 20B** | [🤗internlm/internlm-chat-20b](https://huggingface.co/internlm/internlm-20b-chat) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-chat-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-20b-chat/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-20b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-20b-original) | 2023-12-12 |
| **InternLM 20B** | [🤗internlm/internlm-20b](https://huggingface.co/internlm/internlm-20b) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-20b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-20b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-20b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-20b-original) | 2023-09-20 |
## Performance Evaluation

4
model_cards/internlm_7b.md
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@ -12,8 +12,8 @@ InternLM-7B contains a 7 billion parameter base model and a chat model tailored
| Model | Transformers(HF) | ModelScope(HF) | OpenXLab(HF) | OpenXLab(Original) | Release Date |
|---------------------------|------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------|--------------|
| **InternLM Chat 7B** | [🤗internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) | [<img src="./docs/imgs/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-original) | 2023-12-12 |
| **InternLM 7B** | [🤗internlm/internlm-7b](https://huggingface.co/internlm/internlm-7b) | [<img src="./docs/imgs/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b-original) | 2023-07-06 |
| **InternLM Chat 7B** | [🤗internlm/internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-chat-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-chat-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-chat-7b-original) | 2023-12-12 |
| **InternLM 7B** | [🤗internlm/internlm-7b](https://huggingface.co/internlm/internlm-7b) | [<img src="../assets/modelscope_logo.png" width="20px" /> Shanghai_AI_Laboratory/internlm-7b](https://modelscope.cn/models/Shanghai_AI_Laboratory/internlm-7b/summary) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b) | [![Open in OpenXLab](https://cdn-static.openxlab.org.cn/header/openxlab_models.svg)](https://openxlab.org.cn/models/detail/OpenLMLab/InternLM-7b-original) | 2023-07-06 |
## Performance Evaluation