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