LoRA trained on a thinking/reasoning and roleplaying dataset and then merged with the Qwen2.5-7B-Instruct-1M model, which supports up to 1 million token context lengths.
What this Model Can Do:
- Roleplay: Engage in creative conversations and storytelling!
- Reasoning: Tackle problems and answer your questions in a logical way (thanks to the LoRA layer).
- Thinking: Use the
<think>tag in your system prompts to activate the model's thinking abilities.
Merge Method
This model was merged using the Passthrough merge method using Qwen/Qwen2.5-7B-Instruct-1M + bunnycore/Qwen-2.5-7B-1M-RRP-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: Qwen/Qwen2.5-7B-Instruct-1M+bunnycore/Qwen-2.5-7B-1M-RRP-v1-lora
dtype: bfloat16
merge_method: passthrough
models:
- model: Qwen/Qwen2.5-7B-Instruct-1M+bunnycore/Qwen-2.5-7B-1M-RRP-v1-lora
tokenizer_source: Qwen/Qwen2.5-7B-Instruct-1M
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 32.96 |
| IFEval (0-Shot) | 74.81 |
| BBH (3-Shot) | 35.65 |
| MATH Lvl 5 (4-Shot) | 28.17 |
| GPQA (0-shot) | 7.05 |
| MuSR (0-shot) | 15.80 |
| MMLU-PRO (5-shot) | 36.29 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard74.810
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard35.650
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard28.170
- acc_norm on GPQA (0-shot)Open LLM Leaderboard7.050
- acc_norm on MuSR (0-shot)Open LLM Leaderboard15.800
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard36.290