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xtuner/configs/internlm/internlm2_chat_1_8b/hybrid/agent.json
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
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| { | ||
| "messages": [ | ||
| {"role": "system", "content": "You are InternLM2-Chat, a harmless AI assistant"}, | ||
| { | ||
| "role": "user", | ||
| "content": "Please help me process and visualize this dataset.", | ||
| "files": [{"path": "data.csv", "size": "10K"}] | ||
| }, | ||
| { | ||
| "role": "assistant", | ||
| "content": "I have processed the data and visualized it for you.", | ||
| "code_interpreter_call": "```python\nimport plotly.express as px\nimport pandas as pd\n\n# Load the data into a pandas dataframe\ndf = pd.read_csv('data.csv')\n\n# Create a scatter plot of rainfall vs wind direction\nfig = px.scatter(df, x='WindDir9am', y='Rainfall', color='WindDir3pm',\n labels={'WindDir9am': 'Wind Direction 9am', 'Rainfall': '\n\nRainfall', 'WindDir3pm': 'Wind Direction 3pm'},\n title='Rainfall vs Wind Direction',\n template='plotly_dark',\n width=600, height=500)\n\n# Add a hover effect to show the date\nfig.update_traces(hovertemplate='<b>Date: %{text}</b><br>Wind Direction 9am: %{x}<br>Rainfall: %{y}<br>Wind Direction 3pm: %{marker.color}')\n\n# Show the plot\nfig.show()\n```" | ||
| }, | ||
| { | ||
| "role": "code_interpreter", | ||
| "content": "" | ||
| }, | ||
| { | ||
| "role": "assistant", | ||
| "content": "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." | ||
| }, | ||
| { | ||
| "role": "user", | ||
| "content": "I want to know today's weather in Shanghai" | ||
| }, | ||
| { | ||
| "role": "assistant", | ||
| "content": "Sure, I will search for the weather of Shanghai.", | ||
| "function_call": { | ||
| "name": "get_current_weather", | ||
| "parameters": {"location": "Shanghai"} | ||
| } | ||
| }, | ||
| { | ||
| "role": "function", | ||
| "name": "get_current_weather", | ||
| "content": "{'temperature': 22}" | ||
| }, | ||
| { | ||
| "role": "assistant", | ||
| "content": "The weather in Shanghai is 22 celsius" | ||
| } | ||
| ], | ||
|
|
||
| "functions": [ | ||
| { | ||
| "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"] | ||
| } | ||
| } | ||
| } | ||
| ], | ||
|
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| "code_interpreter": "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:\n- Data analysis or processing (such as data manipulation and graphic creation)\n- Complex calculations (such as math and physics problems)\n- Programming examples (for understanding programming concepts or language features)\n- Text processing and analysis (including text analysis and natural language processing)\n- Machine learning and data science (model training and data visualization)\n- File operations and data import (handling CSV, JSON, etc. formats)"} |
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xtuner/configs/internlm/internlm2_chat_1_8b/hybrid/example.py
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| import json | ||
|
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| from xtuner.types import HybridChatTemplate, TrainingHybridChatMessages | ||
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| chat_template = HybridChatTemplate( | ||
| system='<|im_start|>system\n{system}<|im_end|>\n', | ||
| user='<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n', | ||
| assistant='{assistant}<|im_end|>\n', | ||
| stop_words=['<|im_end|>'], | ||
| image_token='<image>', | ||
| files='<|im_start|>user name=file\n{files}<|im_end|>\n', | ||
| function_call='{assistant}<|action_start|><|plugin|>\n{function_call}<|action_end|><|im_end|>\n', # noqa: E501, E251 | ||
| function_result='<|im_start|>environment name=<|plugin|>\n{function_result}<|im_end|>\n<|im_start|>assistant\n', # noqa: E501, E251 | ||
| functions='<|im_start|>system name=<|plugin|>\n{functions}<|im_end|>\n', | ||
| code_interpreter_call='{assistant}<|action_start|><|interpreter|>\n{code_interpreter_call}<|action_end|><|im_end|>\n', # noqa: E501, E251 | ||
| code_interpreter_result='<|im_start|>environment name=<|interpreter|>\n{code_interpreter_result}<|im_end|>\n<|im_start|>assistant\n', # noqa: E501, E251 | ||
| code_interpreter='<|im_start|>system name=<|interpreter|>\n{code_interpreter}<|im_end|>\n' | ||
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| ) | ||
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| agent_data = json.load(open('agent.json')) | ||
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| msg = TrainingHybridChatMessages.from_dict(agent_data) | ||
| print(msg.apply_chat_template(chat_template)) | ||
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| from transformers import AutoTokenizer | ||
| tokenizer = AutoTokenizer.from_pretrained('internlm/internlm2-chat-7b', trust_remote_code=True) | ||
| print(msg.tokenize(tokenizer, chat_template)) |
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