Phi-4-Stock-RP is a phi4 based language model designed for reasoning and role-play scenarios. It leverages the capabilities of several pre-existing high-quality models, integrating them into a cohesive system that excels in reasoning, creative, narrative, and interactive text generation.
Training Data:
Sources: Merged from various pre-trained models, focusing on those with strong performance in text generation and understanding. Enhanced with a specialized LoRA trained on role-play dialogues, scenarios, and character interactions. Model Capabilities:
Role-Playing: Capable of maintaining coherent characters, plots, and dialogues over extended interactions. Creative Writing: Assists in crafting stories, dialogues, and character development with a focus on immersion and narrative coherence. General Language Understanding: Inherits general text comprehension and generation from the base models, making it versatile for various language tasks beyond RP.
Merge Method
This model was merged using the passthrough merge method using bunnycore/Phi-4-Model-Stock + bunnycore/Phi-4-rp-v1-lora as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
base_model: bunnycore/Phi-4-Model-Stock+bunnycore/Phi-4-rp-v1-lora
dtype: bfloat16
merge_method: passthrough
models:
- model: bunnycore/Phi-4-Model-Stock+bunnycore/Phi-4-rp-v1-lora
tokenizer_source: unsloth/phi-4
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 38.73 |
IFEval (0-Shot) | 63.99 |
BBH (3-Shot) | 55.21 |
MATH Lvl 5 (4-Shot) | 32.25 |
GPQA (0-shot) | 14.43 |
MuSR (0-shot) | 18.53 |
MMLU-PRO (5-shot) | 47.96 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard63.990
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard55.210
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard32.250
- acc_norm on GPQA (0-shot)Open LLM Leaderboard14.430
- acc_norm on MuSR (0-shot)Open LLM Leaderboard18.530
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard47.960