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--- |
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language: |
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- pt |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- llama-cpp |
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- gguf-my-repo |
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datasets: |
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- nicholasKluge/instruct-aira-dataset-v3 |
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- cnmoro/GPT4-500k-Augmented-PTBR-Clean |
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- rhaymison/orca-math-portuguese-64k |
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- nicholasKluge/reward-aira-dataset |
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metrics: |
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- perplexity |
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pipeline_tag: text-generation |
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widget: |
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- text: <instruction>Cite algumas bandas de rock brasileiras famosas.</instruction> |
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example_title: Exemplo |
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- text: <instruction>Invente uma história sobre um encanador com poderes mágicos.</instruction> |
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example_title: Exemplo |
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- text: <instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction> |
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example_title: Exemplo |
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- text: <instruction>Diga o nome de uma maravilha culinária característica da cosinha |
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Portuguesa?</instruction> |
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example_title: Exemplo |
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inference: |
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parameters: |
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repetition_penalty: 1.2 |
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temperature: 0.2 |
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top_k: 20 |
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top_p: 0.2 |
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max_new_tokens: 150 |
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co2_eq_emissions: |
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emissions: 42270 |
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source: CodeCarbon |
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training_type: pre-training |
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geographical_location: Germany |
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hardware_used: NVIDIA A100-SXM4-80GB |
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base_model: TucanoBR/Tucano-2b4-Instruct |
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model-index: |
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- name: Tucano-2b4-Instruct |
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results: |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: CALAME-PT |
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type: NOVA-vision-language/calame-pt |
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split: all |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc |
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value: 57.66 |
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name: accuracy |
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source: |
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url: https://huggingface.co/datasets/NOVA-vision-language/calame-pt |
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name: Context-Aware LAnguage Modeling Evaluation for Portuguese |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: LAMBADA-PT |
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type: TucanoBR/lambada-pt |
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split: train |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: acc |
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value: 39.92 |
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name: accuracy |
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source: |
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url: https://huggingface.co/datasets/TucanoBR/lambada-pt |
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name: LAMBADA-PT |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: ENEM Challenge (No Images) |
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type: eduagarcia/enem_challenge |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 20.43 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: BLUEX (No Images) |
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type: eduagarcia-temp/BLUEX_without_images |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 22.81 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: OAB Exams |
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type: eduagarcia/oab_exams |
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split: train |
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args: |
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num_few_shot: 3 |
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metrics: |
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- type: acc |
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value: 24.83 |
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name: accuracy |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Assin2 RTE |
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type: assin2 |
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split: test |
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args: |
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num_few_shot: 15 |
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metrics: |
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- type: f1_macro |
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value: 43.39 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Assin2 STS |
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type: eduagarcia/portuguese_benchmark |
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split: test |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: pearson |
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value: 6.31 |
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name: pearson |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: FaQuAD NLI |
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type: ruanchaves/faquad-nli |
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split: test |
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args: |
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num_few_shot: 15 |
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metrics: |
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- type: f1_macro |
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value: 43.97 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HateBR Binary |
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type: ruanchaves/hatebr |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 27.7 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: PT Hate Speech Binary |
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type: hate_speech_portuguese |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 29.18 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: tweetSentBR |
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type: eduagarcia-temp/tweetsentbr |
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split: test |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: f1_macro |
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value: 43.11 |
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name: f1-macro |
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source: |
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url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard |
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name: Open Portuguese LLM Leaderboard |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: ARC-Challenge (PT) |
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type: arc_pt |
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args: |
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num_few_shot: 25 |
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metrics: |
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- type: acc_norm |
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value: 32.05 |
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name: normalized accuracy |
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source: |
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url: https://github.com/nlp-uoregon/mlmm-evaluation |
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name: Evaluation Framework for Multilingual Large Language Models |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: HellaSwag (PT) |
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type: hellaswag_pt |
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args: |
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num_few_shot: 10 |
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metrics: |
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- type: acc_norm |
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value: 48.28 |
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name: normalized accuracy |
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source: |
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url: https://github.com/nlp-uoregon/mlmm-evaluation |
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name: Evaluation Framework for Multilingual Large Language Models |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: TruthfulQA (PT) |
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type: truthfulqa_pt |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: mc2 |
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value: 38.44 |
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name: bleurt |
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source: |
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url: https://github.com/nlp-uoregon/mlmm-evaluation |
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name: Evaluation Framework for Multilingual Large Language Models |
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- task: |
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type: text-generation |
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name: Text Generation |
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dataset: |
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name: Alpaca-Eval (PT) |
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type: alpaca_eval_pt |
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args: |
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num_few_shot: 0 |
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metrics: |
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- type: lc_winrate |
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value: 13.0 |
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name: length controlled winrate |
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source: |
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url: https://github.com/tatsu-lab/alpaca_eval |
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name: AlpacaEval |
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--- |
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|
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# cnmoro/Tucano-2b4-Instruct-Q4_K_M-GGUF |
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This model was converted to GGUF format from [`TucanoBR/Tucano-2b4-Instruct`](https://huggingface.co/TucanoBR/Tucano-2b4-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. |
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Refer to the [original model card](https://huggingface.co/TucanoBR/Tucano-2b4-Instruct) for more details on the model. |
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## Use with llama.cpp |
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Install llama.cpp through brew (works on Mac and Linux) |
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```bash |
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brew install llama.cpp |
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``` |
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Invoke the llama.cpp server or the CLI. |
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### CLI: |
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```bash |
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llama-cli --hf-repo cnmoro/Tucano-2b4-Instruct-Q4_K_M-GGUF --hf-file tucano-2b4-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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### Server: |
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```bash |
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llama-server --hf-repo cnmoro/Tucano-2b4-Instruct-Q4_K_M-GGUF --hf-file tucano-2b4-instruct-q4_k_m.gguf -c 2048 |
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``` |
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. |
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Step 1: Clone llama.cpp from GitHub. |
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``` |
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git clone https://github.com/ggerganov/llama.cpp |
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``` |
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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). |
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``` |
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cd llama.cpp && LLAMA_CURL=1 make |
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``` |
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Step 3: Run inference through the main binary. |
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``` |
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./llama-cli --hf-repo cnmoro/Tucano-2b4-Instruct-Q4_K_M-GGUF --hf-file tucano-2b4-instruct-q4_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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or |
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``` |
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./llama-server --hf-repo cnmoro/Tucano-2b4-Instruct-Q4_K_M-GGUF --hf-file tucano-2b4-instruct-q4_k_m.gguf -c 2048 |
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``` |
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