README.md

metadata

library_name: transformers
tags:
  - mergekit
  - merge
  - TensorBlock
  - GGUF
base_model: djuna/L3.1-Suze-Vume-calc
model-index:
  - name: L3.1-Suze-Vume-calc
    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: 72.97
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          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: 31.14
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          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: 9.89
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          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: 4.25
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          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: 8.3
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          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: 27.94
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/L3.1-Suze-Vume-calc
          name: Open LLM Leaderboard

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djuna/L3.1-Suze-Vume-calc - GGUF

This repo contains GGUF format model files for djuna/L3.1-Suze-Vume-calc.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Run them on the TensorBlock client using your local machine ↗

Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
L3.1-Suze-Vume-calc-Q2_K.gguf	Q2_K	3.179 GB	smallest, significant quality loss - not recommended for most purposes
L3.1-Suze-Vume-calc-Q3_K_S.gguf	Q3_K_S	3.665 GB	very small, high quality loss
L3.1-Suze-Vume-calc-Q3_K_M.gguf	Q3_K_M	4.019 GB	very small, high quality loss
L3.1-Suze-Vume-calc-Q3_K_L.gguf	Q3_K_L	4.322 GB	small, substantial quality loss
L3.1-Suze-Vume-calc-Q4_0.gguf	Q4_0	4.661 GB	legacy; small, very high quality loss - prefer using Q3_K_M
L3.1-Suze-Vume-calc-Q4_K_S.gguf	Q4_K_S	4.693 GB	small, greater quality loss
L3.1-Suze-Vume-calc-Q4_K_M.gguf	Q4_K_M	4.921 GB	medium, balanced quality - recommended
L3.1-Suze-Vume-calc-Q5_0.gguf	Q5_0	5.599 GB	legacy; medium, balanced quality - prefer using Q4_K_M
L3.1-Suze-Vume-calc-Q5_K_S.gguf	Q5_K_S	5.599 GB	large, low quality loss - recommended
L3.1-Suze-Vume-calc-Q5_K_M.gguf	Q5_K_M	5.733 GB	large, very low quality loss - recommended
L3.1-Suze-Vume-calc-Q6_K.gguf	Q6_K	6.596 GB	very large, extremely low quality loss
L3.1-Suze-Vume-calc-Q8_0.gguf	Q8_0	8.541 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/L3.1-Suze-Vume-calc-GGUF --include "L3.1-Suze-Vume-calc-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/L3.1-Suze-Vume-calc-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'