| Name | chatbot-kernel JSON |
| Version |
0.3.3
JSON |
| download |
| home_page | None |
| Summary | A Jupyter kernel using LLM models from Huggingface |
| upload_time | 2024-08-27 15:25:10 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | BSD 3-Clause License
Copyright (c) 2024, Chia-Jung Hsu
Redistribution and use in source and binary forms, with or without
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# chatbot_kernel
A Jupyter kernel to use Jupyter as a chat window, running downloaded LLMs locally.
## Installation
This kernel package can be install from PyPI
```
$ pip install chatbot_kernel
```
Once the package installed, the kernel spec can be installed to home directory by command:
```
$ python -m chatbot_kernel install --user
```
If you are using virtualenv, do
```
$ python -m chatbot_kernel install --sys-prefix
```
instead.
If you install the package to a virtual environment, you may need to set up `JUPYTER_PATH=/path/to/venv/share/jupyter:$JUPYTER_PATH` so that jupyter can find the kernel
## Usage
A few magics are available in the kernel:
- `%help`: Print help messages
- `%config`: Set advanced configuration. See more in `%config help`
- `%load`: Load a pretrained LLM
- `%hf_home`: Set the path to find downloaded LLMs, similar to set `HF_HOME` environment variable
- `%model_list`: Show the available LLMs
- `%new_chat`: Clean up the chat history
Before start chatting, you need to at least download a model from [HuggingFace](https://huggingface.co/docs/hub/models-downloading). For example, `huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct`.
Once models are downloaded, launch a jupyter notebook/lab and execute `%load <model>` and start chatting. Here is an example:
```
%load meta-llama/Meta-Llama-3-8B-Instruct
hi
who are you
```
## Caveat
Currently, the kernel use the `AutoModelForCausalLM` and it is not supported by all models.
A few models have been tested:
- `meta-llama/Meta-Llama-3-8B-Instruct`
- `mistralai/Mistral-7B-Instruct-v0.3`: needs `sentencepiece` dependency
- `unsloth/llama-3-8b-Instruct-bnb-4bit`: needs `bitsandbytes` dependency
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"description": "# chatbot_kernel\nA Jupyter kernel to use Jupyter as a chat window, running downloaded LLMs locally.\n\n## Installation\nThis kernel package can be install from PyPI\n```\n$ pip install chatbot_kernel\n```\n\nOnce the package installed, the kernel spec can be installed to home directory by command:\n```\n$ python -m chatbot_kernel install --user\n```\nIf you are using virtualenv, do\n```\n$ python -m chatbot_kernel install --sys-prefix\n```\ninstead.\n\nIf you install the package to a virtual environment, you may need to set up `JUPYTER_PATH=/path/to/venv/share/jupyter:$JUPYTER_PATH` so that jupyter can find the kernel\n\n## Usage\nA few magics are available in the kernel:\n- `%help`: Print help messages\n- `%config`: Set advanced configuration. See more in `%config help`\n- `%load`: Load a pretrained LLM\n- `%hf_home`: Set the path to find downloaded LLMs, similar to set `HF_HOME` environment variable\n- `%model_list`: Show the available LLMs\n- `%new_chat`: Clean up the chat history\n\nBefore start chatting, you need to at least download a model from [HuggingFace](https://huggingface.co/docs/hub/models-downloading). For example, `huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct`.\nOnce models are downloaded, launch a jupyter notebook/lab and execute `%load <model>` and start chatting. Here is an example:\n```\n%load meta-llama/Meta-Llama-3-8B-Instruct\nhi \nwho are you\n```\n\n## Caveat\nCurrently, the kernel use the `AutoModelForCausalLM` and it is not supported by all models.\nA few models have been tested:\n- `meta-llama/Meta-Llama-3-8B-Instruct`\n- `mistralai/Mistral-7B-Instruct-v0.3`: needs `sentencepiece` dependency\n- `unsloth/llama-3-8b-Instruct-bnb-4bit`: needs `bitsandbytes` dependency\n\n",
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