初始化项目，由ModelHub XC社区提供模型

Model: digitous/GPT-R
Source: Original Platform

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README.md

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+---
+license: bigscience-openrail-m
+language:
+- en
+---
+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

config.json

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+{
+  "_name_or_path": "digitous/GPT-R",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPTJForCausalLM"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "gradient_checkpointing": false,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gptj",
+  "n_embd": 4096,
+  "n_head": 16,
+  "n_inner": null,
+  "n_layer": 28,
+  "n_positions": 2048,
+  "resid_pdrop": 0.0,
+  "rotary": true,
+  "rotary_dim": 64,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50,
+      "temperature": 1.0
+    }
+  },
+  "tie_word_embeddings": false,
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float16",
+  "transformers_version": "4.24.0",
+  "use_cache": true,
+  "vocab_size": 50400,
+  "welcome": "## GPT-R is an experimental merge of reciprocate/ppo_hh_gpt-j and togethercomputer/GPT-JT-6B-v1."
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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