| --- |
| license: bigscience-openrail-m |
| language: |
| - en |
| --- |
| GPT-R [Ronin] |
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| 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. |
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| -Intended Merge Value- |
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| 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. |
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| -Intended Use- |
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| 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. |
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| 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. |
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| 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. |
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| -Credits To- |
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| Core Model: |
| https://huggingface.co/EleutherAI/gpt-j-6B |
| Author: |
| https://www.eleuther.ai/ |
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| Model1; 60% ppo_hh_gpt-j: |
| https://huggingface.co/reciprocate/ppo_hh_gpt-j |
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| Author Repo: |
| https://huggingface.co/reciprocate |
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| Related; CarperAI: |
| https://huggingface.co/CarperAI |
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| 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 |
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| PPO explained: |
| https://paperswithcode.com/method/ppo |
| Potential HH-type datasets utilized: |
| https://huggingface.co/HuggingFaceH4 |
| https://huggingface.co/datasets/Anthropic/hh-rlhf |
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| Model2; 40% GPT-JT-6B-V1: |
| https://huggingface.co/togethercomputer/GPT-JT-6B-v1 |
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| Author Repo: |
| https://huggingface.co/togethercomputer |
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| Related; BigScience: |
| https://huggingface.co/bigscience |
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| 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 |
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| Weight merge Script credit to Concedo: |
| https://huggingface.co/concedo |
