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metadata
license: apache-2.0
tags:
  - TensorBlock
  - GGUF
base_model: w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
model-index:
  - name: SOLAR-10.7B-Instruct-v1.0-uncensored
    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: 38.84
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          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: 33.86
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          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: 0.23
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          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: 5.93
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          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: 18.49
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          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: 26.04
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored
          name: Open LLM Leaderboard
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w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored - GGUF

This repo contains GGUF format model files for w4r10ck/SOLAR-10.7B-Instruct-v1.0-uncensored.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Prompt template

Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.

Model file specification

Filename Quant type File Size Description
SOLAR-10.7B-Instruct-v1.0-uncensored-Q2_K.gguf Q2_K 4.003 GB smallest, significant quality loss - not recommended for most purposes
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_S.gguf Q3_K_S 4.665 GB very small, high quality loss
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_M.gguf Q3_K_M 5.196 GB very small, high quality loss
SOLAR-10.7B-Instruct-v1.0-uncensored-Q3_K_L.gguf Q3_K_L 5.651 GB small, substantial quality loss
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_0.gguf Q4_0 6.072 GB legacy; small, very high quality loss - prefer using Q3_K_M
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_K_S.gguf Q4_K_S 6.119 GB small, greater quality loss
SOLAR-10.7B-Instruct-v1.0-uncensored-Q4_K_M.gguf Q4_K_M 6.462 GB medium, balanced quality - recommended
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_0.gguf Q5_0 7.397 GB legacy; medium, balanced quality - prefer using Q4_K_M
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_K_S.gguf Q5_K_S 7.397 GB large, low quality loss - recommended
SOLAR-10.7B-Instruct-v1.0-uncensored-Q5_K_M.gguf Q5_K_M 7.598 GB large, very low quality loss - recommended
SOLAR-10.7B-Instruct-v1.0-uncensored-Q6_K.gguf Q6_K 8.805 GB very large, extremely low quality loss
SOLAR-10.7B-Instruct-v1.0-uncensored-Q8_0.gguf Q8_0 11.404 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/w4r10ck_SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF --include "SOLAR-10.7B-Instruct-v1.0-uncensored-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/w4r10ck_SOLAR-10.7B-Instruct-v1.0-uncensored-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'