README.md

---
license: apache-2.0
library_name: transformers
base_model:
- Qwen/Qwen2.5-14B-Instruct
model-index:
- name: Rombos-LLM-V2.6-Qwen-14b
  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: 52.14
      name: strict accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      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: 49.22
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      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: 28.85
      name: exact match
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      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: 17.0
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      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: 19.26
      name: acc_norm
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      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: 48.85
      name: accuracy
    source:
      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
      name: Open LLM Leaderboard
---
# Rombos-LLM-V2.5-Qwen-14b 

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/LbnAeRIHQhRH_dVxfcHOw.jpeg)

Rombos-LLM-V2.6-Qwen-14b is the upgraded version of "rombodawg/Rombos-LLM-V2.5-Qwen-14b". The magic I performed to make this model better than it already was is only known to the Deepest state, dankest memers and God himself, so dont ask 😉. But it does perform a decent bit better than version 2.5 from my hand testing. Benchmarks will come later.

Check out the Continuous Finetuning method that I apply to all my models bellow:

- https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing

Quants:

- https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b-Q8_0-GGUF

- https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b-Q5_K_M-GGUF

- https://huggingface.co/bartowski/Rombos-LLM-V2.6-Qwen-14b-GGUF

Benchmarks: 
# [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-V2.6-Qwen-14b)

|      Metric       |Value|
|-------------------|----:|
|Avg.               |35.89|
|IFEval (0-Shot)    |52.14|
|BBH (3-Shot)       |49.22|
|MATH Lvl 5 (4-Shot)|28.85|
|GPQA (0-shot)      |17.00|
|MuSR (0-shot)      |19.26|
|MMLU-PRO (5-shot)  |48.85|