japanese-gpt-neox-3.6b-instruction-ppo
Maintainer: rinna
69
🛠️
| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The japanese-gpt-neox-3.6b-instruction-ppo model is a Japanese language model developed by rinna Co., Ltd. It is a 36-layer, 2816-hidden-size transformer-based language model that has been fine-tuned using Reinforcement Learning from Human Feedback (RLHF) to better follow instructions.
This model is part of a series that includes the japanese-gpt-neox-3.6b-instruction-sft-v2 and japanese-gpt-neox-3.6b models. The PPO (Proximal Policy Optimization) version has shown improved performance over the earlier Supervised Fine-Tuning (SFT) versions, based on human evaluation and automated ChatGPT-based evaluation.
Model inputs and outputs
Inputs
- The model takes in conversational prompts formatted as a series of dialog exchanges, with each line indicating the speaker and their text.
- A special newline symbol
<NL>is used to separate the utterances. - The input prompt is ended with a colon to signal the model to generate a response.
Outputs
- The model generates coherent, contextual Japanese text responses to continue the conversation.
- Outputs are decoded from the model's token IDs, with the special newline symbol replaced with actual newlines.
Capabilities
The japanese-gpt-neox-3.6b-instruction-ppo model has been trained to follow instructions and engage in open-ended dialog. It can be used for a variety of Japanese language tasks, such as:
- Generating human-like Japanese text responses to prompts
- Assisting with Japanese language comprehension and generation
- Providing informative and on-topic responses to questions
What can I use it for?
This model could be useful for building Japanese chatbots, virtual assistants, or other language-based applications. The RLHF fine-tuning makes it well-suited for applications that require the model to follow specific instructions or guidelines.
Some potential use cases include:
- Japanese customer service chatbots
- Language learning tools and tutors
- Japanese research assistants
- Creative writing aids for Japanese authors
Things to try
One interesting aspect of this model is how the RLHF fine-tuning has impacted its behavior compared to the earlier SFT versions. You could try prompting the model with the same inputs across the different variants to see how the responses differ. This could provide insights into the effects of the reinforcement learning approach.
Additionally, you could experiment with different generation parameters, such as temperature and top-p sampling, to see how they influence the model's output. This could help you find the sweet spot for your particular use case.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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