Create README.md

README.md

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+---
+language: en
+license: apache-2.0
+commercial: 'no'
+inference: false
+---
+# GPT-J 6B - Dolly_Shygmalion-6b-Dev_V8P2 Mix
+## Model description
+This is a merged model, using a weighted parameter blend strategy at a (20:20:60) ratio between the models:
+
+- [20%] - KoboldAI/GPT-J-6B-Shinen: https://huggingface.co/KoboldAI/GPT-J-6B-Shinen
+- [20%] - databricks/dolly-v1-6b: https://huggingface.co/databricks/dolly-v1-6b
+- [60%] - Pygmalion/Pygmalion-6b DEV (V8 / Part 2): https://huggingface.co/Pygmalion/Pygmalion-6b
+ 
+By their respective authors.
+
+**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.**
+
+### Intended Use:
+
+Research purposes only, intended for responsible use.
+Express a conversation in natural language, and Dolly_Shygmalion will pick up on the conversational format.
+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 style.
+
+## Information:
+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.
+
+In a similar manner to fine-tuning, merging weights does not add information but transforms it, therefore it is important to consider trade-offs.
+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
+```
+(X*A + Y*B)
+```
+With X & Y being the model weighs, and A/B being how strongly they are represented within the final value. 
+The intent of this is to elevate the end-model by borrowing the strongly represented aspects out of each base model, 
+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.
+
+Blend was done in FP32 and output saved in FP16 for reduced storage needs.
+
+
+## Limitations and biases
+Based on known problems with NLP technology, potential relevant factors include bias (gender, profession, race and religion).
+
+<ins>Warning: This model has a moderate NSFW bias.</ins>
+
+### License
+GPT-J-6b is licensed by EleutherAI under the apache-2.0 license. All Rights Reserved.
+
+### BibTeX entry and citation info
+```
+@misc{gpt-j,
+  author = {Wang, Ben and Komatsuzaki, Aran},
+  title = {{GPT-J-6B: A 6 Billion Parameter Autoregressive Language Model}},
+  howpublished = {\url{https://github.com/kingoflolz/mesh-transformer-jax}},
+  year = 2021,
+  month = May
+}
+```
+
+### Credits To:
+
+Models involved:
+- https://huggingface.co/EleutherAI/gpt-j-6B
+- https://huggingface.co/Pygmalion/Pygmalion-6b
+- https://huggingface.co/reciprocate/ppo_hh_gpt-j
+- https://huggingface.co/KoboldAI/GPT-J-6B-Janeway
+
+Average weights merging Script credit to Concedo:
+- https://huggingface.co/concedo
+
+### Related datasets and articles:
+
+PPO_HH-GPT-J-6b's 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
+
+No formal evaluation is available for this model at this time.
+
+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.
+