初始化项目，由ModelHub XC社区提供模型

Model: TehVenom/GPT-J-Pyg_PPO-6B-Dev-V8p4
Source: Original Platform

README.md

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+---
+license: bigscience-openrail-m
+language:
+- en
+---
+GPT-J-Pyg_PPO-6B [GPT-J Pygmalion Dev V8p4 + GPT-J PPO_HH]
+
+GPT-J-Pyg_PPO-6B is an experimental model containing a parameter-wise 40/60 blend (weighted average PPO_HH:Pygmalion) of the weights of ppo_hh_gpt-j and Pygmalion-6b Dev V8p4.
+
+-Intended Merge Value-
+
+As with fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
+Pyg_PPO combines ppo_hh_gpt-j and Pygmalion-6b; 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 conversation in natural language, and Pyg_PPO will do the thing.
+Try starting a two line prompt such as:
+```
+Bot: "Hello, how are you?"
+You: "I am doing just fine, thank you."
+```
+Or any other
+topic, and the model will carry on in this back and forth format.
+
+Can also be used as a base to merge with other creative,
+technical, 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 0.9, Temperature at 0.6, and Repetition Penalty at 1.1; extra samplers
+disabled.
+
+-Credits To-
+
+Core Model:
+https://huggingface.co/EleutherAI/gpt-j-6B
+Author:
+https://www.eleuther.ai/
+
+Model1; 50% 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; 50% Pygmalion-6b:
+https://huggingface.co/PygmalionAI/pygmalion-6b
+
+Author Repo:
+https://huggingface.co/PygmalionAI
+
+Weight merge Script credit to Concedo:
+https://huggingface.co/concedo
+
+Model's card template credit to Digitous:
+https://huggingface.co/digitous/GPT-R