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.
<|im_start|>system<|im_sep|> {system_message}<|im_end|> <|im_start|>user<|im_sep|> {prompt}<|im_end|> <|im_start|>assistant<|im_sep|>
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