README.md

metadata

license: apache-2.0
library_name: transformers
base_model: macadeliccc/laser-dolphin-mixtral-4x7b-dpo
tags:
  - TensorBlock
  - GGUF
model-index:
  - name: laser-dolphin-mixtral-4x7b-dpo
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 64.93
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 85.81
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 63.04
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 63.77
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 77.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 44.88
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/laser-dolphin-mixtral-4x7b-dpo
          name: Open LLM Leaderboard

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macadeliccc/laser-dolphin-mixtral-4x7b-dpo - GGUF

This repo contains GGUF format model files for macadeliccc/laser-dolphin-mixtral-4x7b-dpo.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

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## Prompt template

Model file specification

Filename	Quant type	File Size	Description
laser-dolphin-mixtral-4x7b-dpo-Q2_K.gguf	Q2_K	8.843 GB	smallest, significant quality loss - not recommended for most purposes
laser-dolphin-mixtral-4x7b-dpo-Q3_K_S.gguf	Q3_K_S	10.433 GB	very small, high quality loss
laser-dolphin-mixtral-4x7b-dpo-Q3_K_M.gguf	Q3_K_M	11.580 GB	very small, high quality loss
laser-dolphin-mixtral-4x7b-dpo-Q3_K_L.gguf	Q3_K_L	12.544 GB	small, substantial quality loss
laser-dolphin-mixtral-4x7b-dpo-Q4_0.gguf	Q4_0	13.624 GB	legacy; small, very high quality loss - prefer using Q3_K_M
laser-dolphin-mixtral-4x7b-dpo-Q4_K_S.gguf	Q4_K_S	13.743 GB	small, greater quality loss
laser-dolphin-mixtral-4x7b-dpo-Q4_K_M.gguf	Q4_K_M	14.610 GB	medium, balanced quality - recommended
laser-dolphin-mixtral-4x7b-dpo-Q5_0.gguf	Q5_0	16.626 GB	legacy; medium, balanced quality - prefer using Q4_K_M
laser-dolphin-mixtral-4x7b-dpo-Q5_K_S.gguf	Q5_K_S	16.626 GB	large, low quality loss - recommended
laser-dolphin-mixtral-4x7b-dpo-Q5_K_M.gguf	Q5_K_M	17.134 GB	large, very low quality loss - recommended
laser-dolphin-mixtral-4x7b-dpo-Q6_K.gguf	Q6_K	19.817 GB	very large, extremely low quality loss
laser-dolphin-mixtral-4x7b-dpo-Q8_0.gguf	Q8_0	25.666 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/laser-dolphin-mixtral-4x7b-dpo-GGUF --include "laser-dolphin-mixtral-4x7b-dpo-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/laser-dolphin-mixtral-4x7b-dpo-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'