Initial commit

README.md

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 ---
+language: en
 license: mit
 ---
+# GPT-Neo 2.7B - Janeway
+## Model Description
+GPT-Neo 2.7B-Janeway is a finetune created using EleutherAI's GPT-Neo 2.7B model.
+## Training data
+The training data contains around 2210 ebooks, mostly in the sci-fi and fantasy genres. The dataset is based on the same dataset used by GPT-Neo-2.7B-Picard, with 20% more data in various genres.
+Some parts of the dataset have been prepended using the following text: `[Genre: <genre1>,<genre2>]`
+### How to use
+You can use this model directly with a pipeline for text generation. This example generates a different sequence each time it's run:
+```py
+>>> from transformers import pipeline
+>>> generator = pipeline('text-generation', model='KoboldAI/GPT-Neo-2.7B-Janeway')
+>>> generator("Welcome Captain Janeway, I apologize for the delay.", do_sample=True, min_length=50)
+[{'generated_text': 'Welcome Captain Janeway, I apologize for the delay."\nIt's all right," Janeway said. "I'm certain that you're doing your best to keep me informed of what\'s going on."'}]
+```
+### Limitations and Biases
+GPT-Neo was trained as an autoregressive language model. This means that its core functionality is taking a string of text and predicting the next token. While language models are widely used for tasks other than this, there are a lot of unknowns with this work.
+GPT-Neo was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. Depending on your usecase GPT-Neo may produce socially unacceptable text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
+As with all language models, it is hard to predict in advance how GPT-Neo will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. 
+### BibTeX entry and citation info
+The model is made using the following software:
+```bibtex
+@software{gpt-neo,
+  author       = {Black, Sid and
+                  Leo, Gao and
+                  Wang, Phil and
+                  Leahy, Connor and
+                  Biderman, Stella},
+  title        = {{GPT-Neo: Large Scale Autoregressive Language 
+                   Modeling with Mesh-Tensorflow}},
+  month        = mar,
+  year         = 2021,
+  note         = {{If you use this software, please cite it using 
+                   these metadata.}},
+  publisher    = {Zenodo},
+  version      = {1.0},
+  doi          = {10.5281/zenodo.5297715},
+  url          = {https://doi.org/10.5281/zenodo.5297715}
+}
+```

config.json

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+{
+  "_name_or_path": "EleutherAI/gpt-neo-2.7B",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPTNeoForCausalLM"
+  ],
+  "attention_dropout": 0,
+  "attention_layers": [
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local",
+    "global",
+    "local"
+  ],
+  "attention_types": [
+    [
+      [
+        "global",
+        "local"
+      ],
+      16
+    ]
+  ],
+  "bos_token_id": 50256,
+  "embed_dropout": 0,
+  "eos_token_id": 50256,
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": null,
+  "layer_norm_epsilon": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neo",
+  "num_heads": 20,
+  "num_layers": 32,
+  "resid_dropout": 0,
+  "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": 0.9
+    }
+  },
+  "tokenizer_class": "GPT2Tokenizer",
+  "torch_dtype": "float16",
+  "transformers_version": "4.17.0.dev0",
+  "use_cache": false,
+  "vocab_size": 50257,
+  "window_size": 256
+}

pytorch_model.bin

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special_tokens_map.json

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+{"bos_token": "<|endoftext|>", "eos_token": "<|endoftext|>", "unk_token": "<|endoftext|>"}

tokenizer_config.json

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+{"errors": "replace", "unk_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "bos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "eos_token": {"content": "<|endoftext|>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true, "__type": "AddedToken"}, "add_prefix_space": false, "model_max_length": 2048, "special_tokens_map_file": null, "name_or_path": "EleutherAI/gpt-neo-2.7B", "tokenizer_class": "GPT2Tokenizer"}