## Dataset Card for "InstructMix"

**Description:**

InstructMix is a versatile instruction-tuning dataset available in Alpaca format. It encompasses a variety of instruction-related tasks and sources, making it well suited for finetuning instruction following Large Language Models.

#### Included Datasets:

| Dataset Name | Size           | Type                                              | Details                                 | GitHub Repo                                     |
|--------------|----------------|---------------------------------------------------|-----------------------------------------|-------------------------------------------------|
| Alpaca_GPT4  | 52,002 examples| General Instruction                              | Generated by GPT-4 using Alpaca          | [GitHub Repo](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)   |
| dolly 2.0    | 15,015 examples| Closed QA, Summarization, etc. (Wikipedia)       | Human Annotated                        | [GitHub Repo](https://github.com/databrickslabs/dolly) |
| Code Alpaca  | 20,022 examples| Code Generation, Editing, Optimization         | Generated by text-davinci-003           | [GitHub Repo](https://github.com/sahil280114/codealpaca) |

Credit for the data source: [alpaca-CoT](https://github.com/PhoebusSi/alpaca-CoT)

#### Dataset Splits:

InstructMix offers several dataset splits, each containing a mix of examples from the mentioned datasets.

1. **instructmix_15k**: 40% Alpaca_GPT4, 40% dolly 2.0, 20% Code Alpaca (15,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
2. **instructmix_30k**: 40% Alpaca_GPT4, 40% dolly 2.0, 20% Code Alpaca (30,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
3. **instructmix_50k**: 40% Alpaca_GPT4, 40% dolly 2.0, 20% Code Alpaca (50,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
4. **instructmix_15k_balanced**: Equal distribution of samples from Alpaca_GPT4, dolly 2.0, and Code Alpaca (15,000 examples)
5. **instructmix_30k_balanced**: Equal distribution of samples from Alpaca_GPT4, dolly 2.0, and Code Alpaca (30,000 examples)
6. **instructmix_all**: All available samples from the mentioned datasets

**Models Trained on InstructMix:**
- [Xilabs/instructmix-llama-3b](https://huggingface.co/Xilabs/instructmix-llama-3b)

**Future Updates:**
The InstructMix family of datasets is a rapidly evolving one, with plans to incorporate more curated data for instruction tuning. The creators are currently developing a new InstructMix dataset that will include conversational data.
task_categories:text-generation size_categories:10K<n<100K language:en instruction-finetuning region:us
{"language": ["en"], "size_categories": ["10K<n<100K"], "task_categories": ["text-generation"], "pretty_name": "InstructMix", "dataset_info": {"features": [{"name": "output", "dtype": "string"}, {"name": "instruction", "dtype": "string"}, {"name": "input", "dtype": "string"}, {"name": "source", "dtype": "string"}], "splits": [{"name": "instructmix_15k", "num_bytes": 10498076, "num_examples": 15000}, {"name": "instructmix_30k", "num_bytes": 21008700, "num_examples": 30000}, {"name": "instructmix_50k", "num_bytes": 34872601, "num_examples": 50000}, {"name": "instructmix_15k_balanced", "num_bytes": 9550701, "num_examples": 15000}, {"name": "instructmix_30k_balanced", "num_bytes": 19149564, "num_examples": 30000}, {"name": "instructmix_all", "num_bytes": 59355817, "num_examples": 87039}], "download_size": 94447900, "dataset_size": 154435459}, "tags": ["instruction-finetuning"]}
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #instruction-finetuning #region-us 

Dataset Card for "InstructMix"
------------------------------


Description:


InstructMix is a versatile instruction-tuning dataset available in Alpaca format. It encompasses a variety of instruction-related tasks and sources, making it well suited for finetuning instruction following Large Language Models.


#### Included Datasets:



Credit for the data source: alpaca-CoT


#### Dataset Splits:


InstructMix offers several dataset splits, each containing a mix of examples from the mentioned datasets.


1. instructmix\_15k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (15,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
2. instructmix\_30k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (30,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
3. instructmix\_50k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (50,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
4. instructmix\_15k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (15,000 examples)
5. instructmix\_30k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (30,000 examples)
6. instructmix\_all: All available samples from the mentioned datasets


Models Trained on InstructMix:


* Xilabs/instructmix-llama-3b


Future Updates:
The InstructMix family of datasets is a rapidly evolving one, with plans to incorporate more curated data for instruction tuning. The creators are currently developing a new InstructMix dataset that will include conversational data.



#### Included Datasets:



Credit for the data source: alpaca-CoT #### Dataset Splits:


InstructMix offers several dataset splits, each containing a mix of examples from the mentioned datasets.


1. instructmix\_15k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (15,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
2. instructmix\_30k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (30,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
3. instructmix\_50k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (50,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
4. instructmix\_15k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (15,000 examples)
5. instructmix\_30k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (30,000 examples)
6. instructmix\_all: All available samples from the mentioned datasets


Models Trained on InstructMix:


* Xilabs/instructmix-llama-3b


Future Updates:
The InstructMix family of datasets is a rapidly evolving one, with plans to incorporate more curated data for instruction tuning. The creators are currently developing a new InstructMix dataset that will include conversational data.
TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #instruction-finetuning #region-us 
 #### Included Datasets:



Credit for the data source: alpaca-CoT #### Dataset Splits:


InstructMix offers several dataset splits, each containing a mix of examples from the mentioned datasets.


1. instructmix\_15k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (15,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
2. instructmix\_30k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (30,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
3. instructmix\_50k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (50,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
4. instructmix\_15k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (15,000 examples)
5. instructmix\_30k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (30,000 examples)
6. instructmix\_all: All available samples from the mentioned datasets


Models Trained on InstructMix:


* Xilabs/instructmix-llama-3b


Future Updates:
The InstructMix family of datasets is a rapidly evolving one, with plans to incorporate more curated data for instruction tuning. The creators are currently developing a new InstructMix dataset that will include conversational data.
passage: TAGS
#task_categories-text-generation #size_categories-10K<n<100K #language-English #instruction-finetuning #region-us 
#### Included Datasets:



Credit for the data source: alpaca-CoT#### Dataset Splits:


InstructMix offers several dataset splits, each containing a mix of examples from the mentioned datasets.


1. instructmix\_15k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (15,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
2. instructmix\_30k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (30,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
3. instructmix\_50k: 40% Alpaca\_GPT4, 40% dolly 2.0, 20% Code Alpaca (50,000 randomly chosen samples according to weightage; in our experience this weightage gives better performance when training LLMs)
4. instructmix\_15k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (15,000 examples)
5. instructmix\_30k\_balanced: Equal distribution of samples from Alpaca\_GPT4, dolly 2.0, and Code Alpaca (30,000 examples)
6. instructmix\_all: All available samples from the mentioned datasets


Models Trained on InstructMix:


* Xilabs/instructmix-llama-3b


Future Updates:
The InstructMix family of datasets is a rapidly evolving one, with plans to incorporate more curated data for instruction tuning. The creators are currently developing a new InstructMix dataset that will include conversational data.