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---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- instruction-finetuning
pretty_name: Shell Command Instruct
---
# **Used to train models that interact directly with shells**

Note: This dataset is out-dated in the llm world, probably easier to just setup a tool with a decent model that supports tooling.

Follow-up details of my process 

- MacOS terminal commands for now. This dataset is still in alpha stages and will be modified.
- Contains 500 somewhat unique training examples so far.
- GPT4 seems like a good candidate for generating more data, licensing would need to be addressed.
- I fine-tuned Solar-10.7B-Instruct-v1.0 with this dataset using a slightly modified version of axolotl. Just a few epochs was enough to get it to output correctly.
- I use oobabooga/text-generation-webui with a custom chat extension for inference. No sandbox is used, it is piped directly into MacOS bash because I'm reckless. C:

- Currently working towards training an MoE (2x7B), multi-modal model (image/text) with this dataset. (BakLLaVA-1-7B + LLaVA-v1.5-7B)

- Inference stages:
  1. Send the instruction to the model, expect command.
  2. Detect shell command and send to sand-boxed shell.
  4. Shell respose should be sent as additional input to model.
  5. The final model response should be sent to user from assistant.
 
TODO:
- Possible "os" column to specify which system the command should be used with, maybe separate datasets for each system type.

## **Sample prompt: (in series, depends on your specific model prompt)**
```
### User:
List files in 'Downloads'
### Command:	
ls ~/Downloads
```
```
### Shell:
file1.pdf file2.txt file3.zip
### Assistant:	
Listing files in 'Downloads': file1.pdf file2.txt file3.zip
```

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	---
	license: apache-2.0
	task_categories:
	- text-generation
	language:
	- en
	tags:
	- instruction-finetuning
	pretty_name: Shell Command Instruct
	---
	# Used to train models that interact directly with shells

	Note: This dataset is out-dated in the llm world, probably easier to just setup a tool with a decent model that supports tooling.

	Follow-up details of my process

	- MacOS terminal commands for now. This dataset is still in alpha stages and will be modified.
	- Contains 500 somewhat unique training examples so far.
	- GPT4 seems like a good candidate for generating more data, licensing would need to be addressed.
	- I fine-tuned Solar-10.7B-Instruct-v1.0 with this dataset using a slightly modified version of axolotl. Just a few epochs was enough to get it to output correctly.
	- I use oobabooga/text-generation-webui with a custom chat extension for inference. No sandbox is used, it is piped directly into MacOS bash because I'm reckless. C:

	- Currently working towards training an MoE (2x7B), multi-modal model (image/text) with this dataset. (BakLLaVA-1-7B + LLaVA-v1.5-7B)

	- Inference stages:
	1. Send the instruction to the model, expect command.
	2. Detect shell command and send to sand-boxed shell.
	4. Shell respose should be sent as additional input to model.
	5. The final model response should be sent to user from assistant.

	TODO:
	- Possible "os" column to specify which system the command should be used with, maybe separate datasets for each system type.

	## Sample prompt: (in series, depends on your specific model prompt)
	```
	### User:
	List files in 'Downloads'

	### Command:
	ls ~/Downloads
	```
	```
	### Shell:
	file1.pdf file2.txt file3.zip

	### Assistant:
	Listing files in 'Downloads': file1.pdf file2.txt file3.zip
	```