94 lines
3.8 KiB
Markdown
94 lines
3.8 KiB
Markdown
---
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language: en
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license: apache-2.0
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commercial: 'no'
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inference: false
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---
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# GPT-J 6B - Dolly_Shygmalion-6b-Dev_V8P2 Mix
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## Model description
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This is a merged model, using a weighted parameter blend strategy at a (20:20:60) ratio between the models:
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- [20%] - KoboldAI/GPT-J-6B-Shinen: https://huggingface.co/KoboldAI/GPT-J-6B-Shinen
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- [20%] - databricks/dolly-v1-6b: https://huggingface.co/databricks/dolly-v1-6b
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- [60%] - Pygmalion/Pygmalion-6b DEV (V8 / Part 2): https://huggingface.co/Pygmalion/Pygmalion-6b
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By their respective authors.
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**Warning: Dolly_Shygmalion-6b-Dev_V8P2 may generate NSFW or inappropriate content due to the base models (Mainly [Pygmalion/Pygmalion-6b V8P2](https://huggingface.co/Pygmalion/Pygmalion-6b)) being trained on general user logs, and internet archives.**
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### Intended Use:
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Research purposes only, intended for responsible use.
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Express a conversation in natural language, and Dolly_Shygmalion will pick up on the conversational format.
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Try starting a two line prompt such as:
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```
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Bot: "Hello, how are you?"
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You: "I am doing just fine, thank you."
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```
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Or any other topic, and the model will carry on in this back and forth style.
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## Information:
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For more details, check out the related source models, especially [Pygmalion/Pygmalion-6b V8P2](https://huggingface.co/Pygmalion/Pygmalion-6b) for more information on how to utilize the chat bot formatting expected.
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In a similar manner to fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
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Dolly_Shygmalion-6b-Dev_V8P2 combines `Dolly-GPT-J`, `Shinen-6b` and `Pygmalion-6b V8P2`; all three models were blended in a two step process using a simple weighted parameter method
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```
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(X*A + Y*B)
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```
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With X & Y being the model weighs, and A/B being how strongly they are represented within the final value.
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The intent of this is to elevate the end-model by borrowing the strongly represented aspects out of each base model,
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but may also weaken other faces of each model, which can be desirable if the base models have problematic traits that need to be worked on.
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Blend was done in FP32 and output saved in FP16 for reduced storage needs.
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## Limitations and biases
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Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
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<ins>Warning: This model has a moderate NSFW bias.</ins>
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### License
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GPT-J-6b is licensed by EleutherAI under the apache-2.0 license. All Rights Reserved.
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### BibTeX entry and citation info
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```
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@misc{gpt-j,
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author = {Wang, Ben and Komatsuzaki, Aran},
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title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
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howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
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year = 2021,
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month = May
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}
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```
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### Credits To:
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Models involved:
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- https://huggingface.co/EleutherAI/gpt-j-6B
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- https://huggingface.co/Pygmalion/Pygmalion-6b
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- https://huggingface.co/reciprocate/ppo_hh_gpt-j
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- https://huggingface.co/KoboldAI/GPT-J-6B-Janeway
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Average weights merging Script credit to Concedo:
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- https://huggingface.co/concedo
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### Related datasets and articles:
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PPO_HH-GPT-J-6b's Dataset is a variant of the Helpful Harmless assistant themed
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dataset and Proximal Policy Optimization, specific datasets
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used are unknown; listed repo datasets include:
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- https://huggingface.co/datasets/reciprocate/summarize_eval_ilql
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- https://huggingface.co/datasets/reciprocate/hh_eval_ilql
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PPO explained:
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- https://paperswithcode.com/method/ppo
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Potential HH-type datasets utilized:
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- https://huggingface.co/HuggingFaceH4
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- https://huggingface.co/datasets/Anthropic/hh-rlhf
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No formal evaluation is available for this model at this time.
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It is recommend to use this model with the KoboldAI software. All feedback and comments can be directed to TeH_Venom on the KoboldAI discord.
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