README.md

metadata

license: apache-2.0
tags:
  - moe
  - mixtral
  - openchat/openchat-3.5-0106
  - giux78/zefiro-7b-beta-ITA-v0.1
  - azale-ai/Starstreak-7b-beta
  - gagan3012/Mistral_arabic_dpo
  - davidkim205/komt-mistral-7b-v1
  - OpenBuddy/openbuddy-zephyr-7b-v14.1
  - manishiitg/open-aditi-hi-v1
  - VAGOsolutions/SauerkrautLM-7b-v1-mistral
  - TensorBlock
  - GGUF
base_model: gagan3012/Multilingual-mistral
model-index:
  - name: Multilingual-mistral
    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: 62.29
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          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: 81.76
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          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: 61.38
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          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: 55.53
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          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: 75.53
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          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: 40.26
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=gagan3012/Multilingual-mistral
          name: Open LLM Leaderboard

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gagan3012/Multilingual-mistral - GGUF

This repo contains GGUF format model files for gagan3012/Multilingual-mistral.

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

Run them on the TensorBlock client using your local machine ↗

Prompt template

<s>[INST] {prompt} [/INST]

Model file specification

Filename	Quant type	File Size	Description
Multilingual-mistral-Q2_K.gguf	Q2_K	17.311 GB	smallest, significant quality loss - not recommended for most purposes
Multilingual-mistral-Q3_K_S.gguf	Q3_K_S	20.433 GB	very small, high quality loss
Multilingual-mistral-Q3_K_M.gguf	Q3_K_M	22.546 GB	very small, high quality loss
Multilingual-mistral-Q3_K_L.gguf	Q3_K_L	24.170 GB	small, substantial quality loss
Multilingual-mistral-Q4_0.gguf	Q4_0	26.444 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Multilingual-mistral-Q4_K_S.gguf	Q4_K_S	26.746 GB	small, greater quality loss
Multilingual-mistral-Q4_K_M.gguf	Q4_K_M	28.448 GB	medium, balanced quality - recommended
Multilingual-mistral-Q5_0.gguf	Q5_0	32.231 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Multilingual-mistral-Q5_K_S.gguf	Q5_K_S	32.231 GB	large, low quality loss - recommended
Multilingual-mistral-Q5_K_M.gguf	Q5_K_M	33.230 GB	large, very low quality loss - recommended
Multilingual-mistral-Q6_K.gguf	Q6_K	38.381 GB	very large, extremely low quality loss
Multilingual-mistral-Q8_0.gguf	Q8_0	49.626 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/Multilingual-mistral-GGUF --include "Multilingual-mistral-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/Multilingual-mistral-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'