README.md

metadata

language:
  - en
license: llama2
tags:
  - Medicine
  - TensorBlock
  - GGUF
datasets:
  - yahma/alpaca-cleaned
base_model: malhajar/meditron-7b-chat
model-index:
  - name: meditron-7b-chat
    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: 50.77
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          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: 75.37
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          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: 40.49
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          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: 48.56
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          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: 73.16
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          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: 9.17
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=malhajar/meditron-7b-chat
          name: Open LLM Leaderboard

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malhajar/meditron-7b-chat - GGUF

This repo contains GGUF format model files for malhajar/meditron-7b-chat.

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
meditron-7b-chat-Q2_K.gguf	Q2_K	2.533 GB	smallest, significant quality loss - not recommended for most purposes
meditron-7b-chat-Q3_K_S.gguf	Q3_K_S	2.948 GB	very small, high quality loss
meditron-7b-chat-Q3_K_M.gguf	Q3_K_M	3.298 GB	very small, high quality loss
meditron-7b-chat-Q3_K_L.gguf	Q3_K_L	3.597 GB	small, substantial quality loss
meditron-7b-chat-Q4_0.gguf	Q4_0	3.826 GB	legacy; small, very high quality loss - prefer using Q3_K_M
meditron-7b-chat-Q4_K_S.gguf	Q4_K_S	3.857 GB	small, greater quality loss
meditron-7b-chat-Q4_K_M.gguf	Q4_K_M	4.081 GB	medium, balanced quality - recommended
meditron-7b-chat-Q5_0.gguf	Q5_0	4.652 GB	legacy; medium, balanced quality - prefer using Q4_K_M
meditron-7b-chat-Q5_K_S.gguf	Q5_K_S	4.652 GB	large, low quality loss - recommended
meditron-7b-chat-Q5_K_M.gguf	Q5_K_M	4.783 GB	large, very low quality loss - recommended
meditron-7b-chat-Q6_K.gguf	Q6_K	5.529 GB	very large, extremely low quality loss
meditron-7b-chat-Q8_0.gguf	Q8_0	7.161 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/malhajar_meditron-7b-chat-GGUF --include "meditron-7b-chat-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/malhajar_meditron-7b-chat-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'