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Browse files- .gitattributes +34 -0
- README.md +59 -0
- config.json +81 -0
- merges.txt +0 -0
- pytorch_model.bin +3 -0
- runs/events.out.tfevents.1672861513.uft-2.228070.0 +3 -0
- runs/events.out.tfevents.1672879694.uft-2.389061.0 +3 -0
- runs/events.out.tfevents.1672880794.uft-2.399187.0 +3 -0
- runs/events.out.tfevents.1672889057.uft-2.472434.0 +3 -0
- special_tokens_map.json +6 -0
- tokenizer.json +0 -0
- tokenizer_config.json +33 -0
- vocab.json +0 -0
.gitattributes
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README.md
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---
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license: creativeml-openrail-m
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language:
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- en
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thumbnail:
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tags:
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- text generation
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- conversational
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inference: false
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---
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# Pygmalion 2.7B
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## Model description
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Pymalion 2.7B is a proof-of-concept dialogue model based on EleutherAI's [gpt-neo-2.7B](https://huggingface.co/EleutherAI/gpt-neo-2.7B).
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**Warning:** This model is **NOT** suitable for use by minors. It **will** output X-rated content under certain circumstances.
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## Training data
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The fine-tuning dataset consisted of 56MB of dialogue data gathered from multiple sources, which includes both real _and_ partially machine-generated conversations.
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## Training procedure
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Model weights were initialized from the `uft-2.7b` ConvoGPT model made available in [this commit](https://huggingface.co/hakurei/convogpt/tree/07707377dee0aa7d1ee5363ef660b13eb5b73f9d/2.7b-uft).
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The model was then further fine-tuned on ~48.5 million tokens for ~5k steps on 4 NVIDIA A40s using DeepSpeed.
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## Intended use
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### The easy way
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We plan to provide a notebook with a Gradio UI for playing around with the model shortly. Until then, please refer to the section below for manual usage.
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### The manual way
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The model can be used as a regular text generation model, but it'll perform best if the input prompt adheres to the following format:
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```
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[CHARACTER]'s Persona: [A few sentences about the character you want the model to play]
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<START>
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[DIALOGUE HISTORY]
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You: [Your input message here]
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[CHARACTER]:
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```
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Where `[CHARACTER]` is, as you can probably guess, the name of the character you want the model to portray, `<START>` should be used verbatim as a delimiter token to separate persona and scenario data from the dialogue, and `[DIALOGUE HISTORY]` is chat history so the model can have some conversational context to draw from. Ideally it'll be pairs of messages like:
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```
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[CHARACTER]: [some dialogue here]
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You: [your response to the dialogue above]
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```
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Apart from chat history, you can also just add example conversations in `[DIALOGUE HISTORY]` to show how the character should speak - ideally at the beginning, so it doesn't get confused as to what's conversation history vs. character definition.
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## Known issues
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We haven't played around with the model enough to enumerate them. Feel free to give us some feedback!
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config.json
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{
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"_name_or_path": "pygmalion-2.7b",
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"activation_function": "gelu_new",
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"architectures": [
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"GPTNeoForCausalLM"
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],
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"attention_dropout": 0,
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"attention_layers": [
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local",
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"global",
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"local"
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],
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"attention_types": [
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[
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[
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"global",
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"local"
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],
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16
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]
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],
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"bos_token_id": 50256,
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"embed_dropout": 0,
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"eos_token_id": 50256,
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"hidden_size": 2560,
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"initializer_range": 0.02,
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"intermediate_size": null,
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"layer_norm_epsilon": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neo",
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"num_heads": 20,
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"num_layers": 32,
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"resid_dropout": 0,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"task_specific_params": {
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"text-generation": {
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"do_sample": true,
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"max_length": 50,
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"temperature": 0.9
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}
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},
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"tokenizer_class": "GPT2Tokenizer",
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"torch_dtype": "float16",
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"transformers_version": "4.26.0.dev0",
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"use_cache": false,
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"vocab_size": 50257,
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"window_size": 256
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}
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merges.txt
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pytorch_model.bin
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special_tokens_map.json
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tokenizer.json
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tokenizer_config.json
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vocab.json
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