Peach-9B-8k-Roleplay

Peach-9B-8k-Roleplay is a fine-tuned large language model dedicated to role-playing conversations. It is based on the 01-ai/Yi-1.5-9B model and is trained on conversations over 100K through data synthesis method. Although the model parameters are small, it may perform best in language models with parameters below 34B.

Demand population:

"The target audience is developers and enthusiasts who need role-playing conversations, such as game developers, script creators, language model researchers, etc. The product can provide rich dialogue generation, helping them quickly build dialogue scenarios and improve their creative efficiency."

Example of usage scenarios:

Game developers use this model to quickly generate character conversations to enrich the game plot.

The script creator uses this model to conduct character dialogue.

Language model researchers use this model to test and study the effects of dialogue generation.

Product Features:

Text generation: Can generate dialogue text that matches the character settings.

Role-playing: Supports users to have role-playing conversations with AI.

Multilingual support: Support Chinese and English dialogue.

Model fine-tuning: fine-tuning is performed based on a large amount of dialogue data to optimize conversation quality.

Parameter optimization: Provides temperature and top_p parameter adjustments to control the diversity and coherence of generated text.

Long conversation support: The training data supports a conversation length of up to 8k.

Tutorials for use:

1. Import the necessary libraries and modules, such as torch and transformers.

2. Load the model and word participle using AutoTokenizer and AutoModelForCausalLM.

3. Prepare conversation messages, including roles and content, and use tokenizer tokenize.

4. Set generation parameters, such as temperature, top_p, etc., to control the characteristics of text generation.

5. Use the model.generate method to generate conversation text.

6. Use the tokenizer.decode method to convert the generated text back to a readable format.