mirror of https://github.com/InternLM/InternLM
110 lines
6.2 KiB
Markdown
110 lines
6.2 KiB
Markdown
|
# 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]
|
||
|
|
|
||
|
|
[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]
|
||
|
|
```
|