
Dolphin 🐬

https://erichartford.com/dolphin


## Dataset details

This dataset is an attempt to replicate the results of [Microsoft's Orca](https://www.microsoft.com/en-us/research/publication/orca-progressive-learning-from-complex-explanation-traces-of-gpt-4/)

Our dataset consists of:

- ~1 million of FLANv2 augmented with GPT-4 completions (flan1m-alpaca-uncensored.jsonl)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (flan5m-alpaca-uncensored.jsonl)


We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.

Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.

Token distribution for GPT-3.5 completions
![dolphin-llama](https://github.com/shahules786/mayavoz/assets/25312635/0a7bfd05-fadf-4eb6-9111-f44c6e53d95d)

### Loading
```python
## load GPT-4 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan1m-alpaca-uncensored.jsonl")

## load GPT-3.5 completions
dataset = load_dataset("ehartford/dolphin",data_files="flan5m-alpaca-uncensored.jsonl")
```


This dataset is licensed apache-2.0 for commercial or non-commercial use.

We currently plan to release Dolphin on:

- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)

The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)

I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:

- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and chirper.ai for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
task_categories:text-generation language:en license:apache-2.0 region:us
{"language": ["en"], "license": "apache-2.0", "task_categories": ["text-generation"], "configs": [{"config_name": "flan1m-alpaca-uncensored", "data_files": "flan1m-alpaca-uncensored.jsonl"}, {"config_name": "flan5m-alpaca-uncensored", "data_files": "flan5m-alpaca-uncensored.jsonl"}]}
TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us 


Dolphin 

URL


## Dataset details

This dataset is an attempt to replicate the results of Microsoft's Orca

Our dataset consists of:

- ~1 million of FLANv2 augmented with GPT-4 completions (URL)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)


We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.

Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.

Token distribution for GPT-3.5 completions
!dolphin-llama

### Loading



This dataset is licensed apache-2.0 for commercial or non-commercial use.

We currently plan to release Dolphin on:

- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)

The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)

I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:

- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
## Dataset details

This dataset is an attempt to replicate the results of Microsoft's Orca

Our dataset consists of:

- ~1 million of FLANv2 augmented with GPT-4 completions (URL)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)


We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.

Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.

Token distribution for GPT-3.5 completions
!dolphin-llama ### Loading



This dataset is licensed apache-2.0 for commercial or non-commercial use.

We currently plan to release Dolphin on:

- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)

The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)

I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:

- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us 
 ## Dataset details

This dataset is an attempt to replicate the results of Microsoft's Orca

Our dataset consists of:

- ~1 million of FLANv2 augmented with GPT-4 completions (URL)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)


We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.

Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.

Token distribution for GPT-3.5 completions
!dolphin-llama ### Loading



This dataset is licensed apache-2.0 for commercial or non-commercial use.

We currently plan to release Dolphin on:

- Xgen 7b 8k
- LLaMA 13b (Non-commercial)
- MPT 30b 8k
- LLaMA 33b (Non-commercial)
- Falcon 40b
- LLaMA 65b (Non-commercial)

The Dolphin models that are released will be subject to the license of the foundational model on which it is trained. (LLaMA releases will be non-commercial)

I would like to thank the motley crew of Open Source AI/ML engineers who have worked beside me in this endeavor. Including:

- Wing "Caseus" Lian and NanoBit of OpenAccess AI Collective
- Rohan
- Teknium
- Pankaj Mathur
- Tom "TheBloke" Jobbins for quantizing and amplifying
- Special thanks to EdenCoder and URL for mentorship and financial sponsorship.
- Special thanks to Kilkonie for his very valued mentorship.
- All the other people in the Open Source AI community who have taught me and helped me along the way.
passage: TAGS
#task_categories-text-generation #language-English #license-apache-2.0 #region-us 
## Dataset details

This dataset is an attempt to replicate the results of Microsoft's Orca

Our dataset consists of:

- ~1 million of FLANv2 augmented with GPT-4 completions (URL)
- ~3.5 million of FLANv2 augmented with GPT-3.5 completions (URL)


We followed the submix and system prompt distribution outlined in the Orca paper. With a few exceptions. We included all 75k of CoT in the FLAN-1m dataset rather than sampling that. Also, we found that many items were duplicated, so we removed duplicates, resulting in 3.5m instructs in the ChatGPT dataset.

Then we filtered out instances of alignment, refusal, avoidance, and bias, in order to produce an uncensored model upon which can be layered your personalized alignment LoRA.

Token distribution for GPT-3.5 completions
!dolphin-llama