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base_model: rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
datasets:
  - rhaymison/superset
language:
  - pt
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
tags:
  - portuguese
  - phi
  - text-generation-inference
  - llama-cpp
  - gguf-my-repo
model-index:
  - name: portuguese-Phi3-Tom-Cat-128k-instruct
    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: 51.15
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 42.56
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 39.86
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 88.86
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 68
            name: pearson
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 45.16
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 85.92
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          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: 65.76
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          name: Open Portuguese LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: tweetSentBR
          type: eduagarcia/tweetsentbr_fewshot
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: f1_macro
            value: 53.32
            name: f1-macro
        source:
          url: >-
            https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct
          name: Open Portuguese LLM Leaderboard

fernandovmacedo/portuguese-Phi3-Tom-Cat-128k-instruct-Q4_K_S-GGUF

This model was converted to GGUF format from rhaymison/portuguese-Phi3-Tom-Cat-128k-instruct 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 llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo fernandovmacedo/portuguese-Phi3-Tom-Cat-128k-instruct-Q4_K_S-GGUF --hf-file portuguese-phi3-tom-cat-128k-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo fernandovmacedo/portuguese-Phi3-Tom-Cat-128k-instruct-Q4_K_S-GGUF --hf-file portuguese-phi3-tom-cat-128k-instruct-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo fernandovmacedo/portuguese-Phi3-Tom-Cat-128k-instruct-Q4_K_S-GGUF --hf-file portuguese-phi3-tom-cat-128k-instruct-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo fernandovmacedo/portuguese-Phi3-Tom-Cat-128k-instruct-Q4_K_S-GGUF --hf-file portuguese-phi3-tom-cat-128k-instruct-q4_k_s.gguf -c 2048