README.md

metadata

language:
  - pt
license: apache-2.0
library_name: transformers
tags:
  - Misral
  - Portuguese
  - 7b
  - llama-cpp
  - gguf-my-repo
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
  - pablo-moreira/gpt4all-j-prompt-generations-pt
  - rhaymison/superset
pipeline_tag: text-generation
model-index:
  - name: Mistral-portuguese-luana-7b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: ENEM Challenge (No Images)
          type: eduagarcia/enem_challenge
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 58.64
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BLUEX (No Images)
          type: eduagarcia-temp/BLUEX_without_images
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 47.98
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: OAB Exams
          type: eduagarcia/oab_exams
          split: train
          args:
            num_few_shot: 3
        metrics:
          - type: acc
            value: 38.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 RTE
          type: assin2
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 90.63
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Assin2 STS
          type: eduagarcia/portuguese_benchmark
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: pearson
            value: 75.81
            name: pearson
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: FaQuAD NLI
          type: ruanchaves/faquad-nli
          split: test
          args:
            num_few_shot: 15
        metrics:
          - type: f1_macro
            value: 57.79
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HateBR Binary
          type: ruanchaves/hatebr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 77.24
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: PT Hate Speech Binary
          type: hate_speech_portuguese
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 68.5
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: tweetSentBR
          type: eduagarcia-temp/tweetsentbr
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 63
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Mistral-portuguese-luana-7b
          name: Open Portuguese LLM Leaderboard

waltervix/Mistral-portuguese-luana-7b-Q4_K_M-GGUF

This model was converted to GGUF format from rhaymison/Mistral-portuguese-luana-7b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with Samantha Interface Assistant

Github project: https://github.com/controlecidadao/samantha_ia/blob/main/README.md

Video: Intelligence Challenge - Microsoft Phi 3.5 vs Google Gemma 2

https://www.youtube.com/watch?v=KgicCGMSygU

👟 Testing a Model in 5 Steps with Samantha

Samantha needs just a .gguf model file to generate text. Follow these steps to perform a simple model test:

1. Open Windows Task Management by pressing CTRL + SHIFT + ESC and check available memory. Close some programs if necessary to free memory.

2. Visit Hugging Face repository and click on the card to open the corresponding page. Locate the Files and versions tab and choose a .gguf model that fits in your available memory.

3. Right click over the model download link icon and copy its URL.

4. Paste the model URL into Samantha's Download models for testing field.

5. Insert a prompt into User prompt field and press Enter. Keep the $$$ sign at the end of your prompt. The model will be downloaded and the response will be generated using the default deterministic settings. You can track this process via Windows Task Management.

Every new model downloaded via this copy and paste procedure will replace the previous one to save hard drive space. Model download is saved as MODEL_FOR_TESTING.gguf in your Downloads folder.

You can also download the model and save it permanently to your computer. For more datails, see the section below.