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---
license: other
tags:
- mergekit
- merge
base_model:
- Qwen/Qwen2.5-3B
- Qwen/Qwen2.5-3B-Instruct
- arcee-ai/raspberry-3B
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct/blob/main/LICENSE
model-index:
- name: Rombos-LLM-V2.5.1-Qwen-3b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 25.95
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 14.88
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 8.31
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 3.24
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 7.82
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 19.1
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.5.1-Qwen-3b
name: Open LLM Leaderboard
---
# Rombos-LLM-V2.5.1-Qwen-3b
A little experiment I threw together to take a really high quality LLM I found (arcee-ai/raspberry-3B) and merge it using the last step of my Continuous Finetuning method outlines in the paper linked bellow.
https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing
Mergekit.yaml file is as follows:
```yaml
models:
- model: Qwen2.5-3B-Instruct
parameters:
weight: 1
density: 1
- model: raspberry-3B
parameters:
weight: 1
density: 1
merge_method: ties
base_model: Qwen2.5-3B
parameters:
weight: 1
density: 1
normalize: true
int8_mask: true
dtype: bfloat16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Rombos-LLM-V2.5.1-Qwen-3b)
| Metric |Value|
|-------------------|----:|
|Avg. |13.22|
|IFEval (0-Shot) |25.95|
|BBH (3-Shot) |14.88|
|MATH Lvl 5 (4-Shot)| 8.31|
|GPQA (0-shot) | 3.24|
|MuSR (0-shot) | 7.82|
|MMLU-PRO (5-shot) |19.10|
|