GPT-R [Ronin]

GPT-R is an experimental model containing a parameter-wise 60/40 blend (weighted average) of the weights of ppo_hh_gpt-j and GPT-JT-6B-v1.

-Intended Merge Value-

As with fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
GPT-Ronin combines ppo_hh_gpt-j and GPT-JT; both technical
achievements are blended with the intent to elevate the strengths of
both. Datasets of both are linked below to assist in exploratory speculation on which datasets in what quantity and configuration have
the largest impact on the usefulness of a model without the expense of
fine-tuning. Blend was done in FP32 and output in FP16.

-Intended Use-

Research purposes only, intended for responsible use.
Express a task in natural language, and GPT-R will do the thing.
Try telling it "Write an article about X but put Y spin on it.",
"Write a five step numbered guide on how to do X.", or any other
basic instructions. It does its best.

Can also be used as a base to merge with conversational,
story writing, or adventure themed models of the same class
(GPT-J & 6b NeoX) and parameter size (6b) to experiment with
the morphology of model weights based on the value added
by instruct.

Merge tested using KoboldAI with Nucleus Sampling Top-P set to 0.7, Temperature at 0.5, and Repetition Penalty at 1.14; extra samplers
disabled.

-Credits To-

Core Model:
https://huggingface.co/EleutherAI/gpt-j-6B
Author:
https://www.eleuther.ai/

Model1; 60% ppo_hh_gpt-j:
https://huggingface.co/reciprocate/ppo_hh_gpt-j

Author Repo:
https://huggingface.co/reciprocate

Related; CarperAI:
https://huggingface.co/CarperAI

Dataset is a variant of the Helpful Harmless assistant themed
dataset and Proximal Policy Optimization, specific datasets
used are unknown; listed repo datasets include:
https://huggingface.co/datasets/reciprocate/summarize_eval_ilql
https://huggingface.co/datasets/reciprocate/hh_eval_ilql

PPO explained:
https://paperswithcode.com/method/ppo
Potential HH-type datasets utilized:
https://huggingface.co/HuggingFaceH4
https://huggingface.co/datasets/Anthropic/hh-rlhf

Model2; 40% GPT-JT-6B-V1:
https://huggingface.co/togethercomputer/GPT-JT-6B-v1

Author Repo:
https://huggingface.co/togethercomputer

Related; BigScience:
https://huggingface.co/bigscience

Datasets:
https://huggingface.co/datasets/the_pile
https://huggingface.co/datasets/bigscience/P3
https://github.com/allenai/natural-instructions
https://ai.googleblog.com/2022/05/language-models-perform-reasoning-via.html

Weight merge Script credit to Concedo:
https://huggingface.co/concedo
{"language": ["en"], "license": "bigscience-openrail-m"}
transformers pytorch gptj text-generation en license:bigscience-openrail-m autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #gptj #text-generation #en #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #has_space #region-us 

GPT-R [Ronin]

GPT-R is an experimental model containing a parameter-wise 60/40 blend (weighted average) of the weights of ppo_hh_gpt-j and GPT-JT-6B-v1.

-Intended Merge Value-

As with fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
GPT-Ronin combines ppo_hh_gpt-j and GPT-JT; both technical
achievements are blended with the intent to elevate the strengths of
both. Datasets of both are linked below to assist in exploratory speculation on which datasets in what quantity and configuration have
the largest impact on the usefulness of a model without the expense of
fine-tuning. Blend was done in FP32 and output in FP16.

-Intended Use-

Research purposes only, intended for responsible use.
Express a task in natural language, and GPT-R will do the thing.
Try telling it "Write an article about X but put Y spin on it.",
"Write a five step numbered guide on how to do X.", or any other
basic instructions. It does its best.

Can also be used as a base to merge with conversational,
story writing, or adventure themed models of the same class
(GPT-J & 6b NeoX) and parameter size (6b) to experiment with
the morphology of model weights based on the value added
by instruct.

Merge tested using KoboldAI with Nucleus Sampling Top-P set to 0.7, Temperature at 0.5, and Repetition Penalty at 1.14; extra samplers
disabled.

-Credits To-

Core Model:
URL
Author:
URL

Model1; 60% ppo_hh_gpt-j:
URL

Author Repo:
URL

Related; CarperAI:
URL

Dataset is a variant of the Helpful Harmless assistant themed
dataset and Proximal Policy Optimization, specific datasets
used are unknown; listed repo datasets include:
URL
URL

PPO explained:
URL
Potential HH-type datasets utilized:
URL
URL

Model2; 40% GPT-JT-6B-V1:
URL

Author Repo:
URL

Related; BigScience:
URL

Datasets:
URL
URL
URL
URL

Weight merge Script credit to Concedo:
URL
TAGS
#transformers #pytorch #gptj #text-generation #en #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #has_space #region-us 

TAGS
#transformers #pytorch #gptj #text-generation #en #license-bigscience-openrail-m #autotrain_compatible #endpoints_compatible #has_space #region-us 
