PsychoCounsel-Llama3-8B-Reward
| Property | Value |
|---|---|
| Base Model | meta-llama/Llama-3.1-8B-Instruct |
| Training Approach | Preference Learning |
| Model Size | 8B parameters |
| Hugging Face | Link |
What is PsychoCounsel-Llama3-8B-Reward?
PsychoCounsel-Llama3-8B-Reward is a specialized reward model developed for psychotherapy applications, built upon the Llama-3.1-8B-Instruct architecture. This model was trained using preference learning on the PsychoCounsel-Preference dataset, enabling it to evaluate and rank therapeutic responses effectively. The model has demonstrated remarkable performance, with the policy model trained using this reward function achieving an 87% win rate against GPT-4 in psycho-counseling tasks.
Implementation Details
The model is implemented using PyTorch and requires specific dependencies including torch 2.5.1, transformers 4.46.3, and openrlhf 0.5.7.dev0. It supports batch processing with BF16 precision and flash attention, allowing for efficient processing of sequences up to 2048 tokens.
- Utilizes flash attention for improved performance
- Supports BF16 precision for efficient computation
- Implements batch processing with configurable batch sizes
- Provides normalized reward scoring for response evaluation
Core Capabilities
- Evaluates therapeutic responses based on learned preferences
- Generates normalized reward scores for comparing response quality
- Processes multi-turn counseling dialogues
- Supports real-time evaluation through API endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on psychotherapy, using preference learning to develop strong capabilities in evaluating therapeutic responses. Its impressive 87% win rate against GPT-4 demonstrates its effectiveness in understanding and assessing counseling interactions.
Q: What are the recommended use cases?
The model is specifically designed for evaluating and ranking therapeutic responses in counseling scenarios. It can be used to train other models through preference learning, assess the quality of AI-generated therapeutic responses, and help improve automated counseling systems.
