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Quantization made by Richard Erkhov. |
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[Github](https://github.com/RichardErkhov) |
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[Discord](https://discord.gg/pvy7H8DZMG) |
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[Request more models](https://github.com/RichardErkhov/quant_request) |
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Replete-LLM-V2.5-Qwen-14b - GGUF |
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- Model creator: https://huggingface.co/Replete-AI/ |
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- Original model: https://huggingface.co/Replete-AI/Replete-LLM-V2.5-Qwen-14b/ |
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| Name | Quant method | Size | |
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| ---- | ---- | ---- | |
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| [Replete-LLM-V2.5-Qwen-14b.Q2_K.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q2_K.gguf) | Q2_K | 5.37GB | |
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| [Replete-LLM-V2.5-Qwen-14b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.IQ3_XS.gguf) | IQ3_XS | 5.94GB | |
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| [Replete-LLM-V2.5-Qwen-14b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.IQ3_S.gguf) | IQ3_S | 6.23GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q3_K_S.gguf) | Q3_K_S | 6.2GB | |
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| [Replete-LLM-V2.5-Qwen-14b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.IQ3_M.gguf) | IQ3_M | 6.44GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q3_K.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q3_K.gguf) | Q3_K | 6.84GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q3_K_M.gguf) | Q3_K_M | 6.84GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q3_K_L.gguf) | Q3_K_L | 7.38GB | |
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| [Replete-LLM-V2.5-Qwen-14b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.IQ4_XS.gguf) | IQ4_XS | 7.62GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q4_0.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q4_0.gguf) | Q4_0 | 7.93GB | |
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| [Replete-LLM-V2.5-Qwen-14b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.IQ4_NL.gguf) | IQ4_NL | 8.01GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q4_K_S.gguf) | Q4_K_S | 7.98GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q4_K.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q4_K.gguf) | Q4_K | 8.37GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q4_K_M.gguf) | Q4_K_M | 8.37GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q4_1.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q4_1.gguf) | Q4_1 | 8.75GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q5_0.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q5_0.gguf) | Q5_0 | 9.56GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q5_K_S.gguf) | Q5_K_S | 9.56GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q5_K.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q5_K.gguf) | Q5_K | 9.79GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q5_K_M.gguf) | Q5_K_M | 9.79GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q5_1.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q5_1.gguf) | Q5_1 | 10.38GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q6_K.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q6_K.gguf) | Q6_K | 11.29GB | |
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| [Replete-LLM-V2.5-Qwen-14b.Q8_0.gguf](https://huggingface.co/RichardErkhov/Replete-AI_-_Replete-LLM-V2.5-Qwen-14b-gguf/blob/main/Replete-LLM-V2.5-Qwen-14b.Q8_0.gguf) | Q8_0 | 14.62GB | |
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Original model description: |
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--- |
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license: apache-2.0 |
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library_name: transformers |
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base_model: |
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- Qwen/Qwen2.5-14B-Instruct |
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model-index: |
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- name: Replete-LLM-V2.5-Qwen-14b |
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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: 58.4 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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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: 49.39 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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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: 15.63 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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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: 16.22 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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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: 18.83 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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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: 48.62 |
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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=Replete-AI/Replete-LLM-V2.5-Qwen-14b |
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name: Open LLM Leaderboard |
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--- |
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# Replete-LLM-V2.5-Qwen-14b |
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ihnWXDEgV-ZKN_B036U1J.png) |
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Replete-LLM-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-14B. I noticed recently that the Qwen team did not learn from my methods of continuous finetuning, the great benefits, and no downsides of it. So I took it upon myself to merge the instruct model with the base model myself using the *Ties* merge method |
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This version of the model shows higher performance than the original instruct and base models. |
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Quants: |
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GGUF: https://huggingface.co/bartowski/Replete-LLM-V2.5-Qwen-14b-GGUF |
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Benchmarks: |
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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_Replete-AI__Replete-LLM-V2.5-Qwen-14b) |
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| Metric |Value| |
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|Avg. |34.52| |
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|IFEval (0-Shot) |58.40| |
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|BBH (3-Shot) |49.39| |
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|MATH Lvl 5 (4-Shot)|15.63| |
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|GPQA (0-shot) |16.22| |
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|MuSR (0-shot) |18.83| |
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|MMLU-PRO (5-shot) |48.62| |
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