README.md

---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
- llama-cpp
- gguf-my-repo
datasets:
- nicholasKluge/instruct-aira-dataset-v3
- cnmoro/GPT4-500k-Augmented-PTBR-Clean
- rhaymison/orca-math-portuguese-64k
- nicholasKluge/reward-aira-dataset
metrics:
- perplexity
pipeline_tag: text-generation
widget:
- text: <instruction>Cite algumas bandas de rock brasileiras famosas.</instruction>
  example_title: Exemplo
- text: <instruction>Invente uma história sobre um encanador com poderes mágicos.</instruction>
  example_title: Exemplo
- text: <instruction>Qual cidade é a capital do estado do Rio Grande do Sul?</instruction>
  example_title: Exemplo
- text: <instruction>Diga o nome de uma maravilha culinária característica da cosinha
    Portuguesa?</instruction>
  example_title: Exemplo
inference:
  parameters:
    repetition_penalty: 1.2
    temperature: 0.2
    top_k: 20
    top_p: 0.2
    max_new_tokens: 150
co2_eq_emissions:
  emissions: 21890
  source: CodeCarbon
  training_type: pre-training
  geographical_location: Germany
  hardware_used: NVIDIA A100-SXM4-80GB
base_model: TucanoBR/Tucano-1b1-Instruct
model-index:
- name: Tucano-1b1-Instruct
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: CALAME-PT
      type: NOVA-vision-language/calame-pt
      split: all
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      value: 56.55
      name: accuracy
    source:
      url: https://huggingface.co/datasets/NOVA-vision-language/calame-pt
      name: Context-Aware LAnguage Modeling Evaluation for Portuguese
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: LAMBADA-PT
      type: TucanoBR/lambada-pt
      split: train
      args:
        num_few_shot: 0
    metrics:
    - type: acc
      value: 35.53
      name: accuracy
    source:
      url: https://huggingface.co/datasets/TucanoBR/lambada-pt
      name: LAMBADA-PT
  - 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: 21.06
      name: accuracy
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 26.01
      name: accuracy
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 26.47
      name: accuracy
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 67.78
      name: f1-macro
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 10
    metrics:
    - type: pearson
      value: 8.88
      name: pearson
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 43.97
      name: f1-macro
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 31.28
      name: f1-macro
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 41.23
      name: f1-macro
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      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: 22.03
      name: f1-macro
    source:
      url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
      name: Open Portuguese LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: ARC-Challenge (PT)
      type: arc_pt
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 30.77
      name: normalized accuracy
    source:
      url: https://github.com/nlp-uoregon/mlmm-evaluation
      name: Evaluation Framework for Multilingual Large Language Models
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (PT)
      type: hellaswag_pt
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 43.5
      name: normalized accuracy
    source:
      url: https://github.com/nlp-uoregon/mlmm-evaluation
      name: Evaluation Framework for Multilingual Large Language Models
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (PT)
      type: truthfulqa_pt
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 41.14
      name: bleurt
    source:
      url: https://github.com/nlp-uoregon/mlmm-evaluation
      name: Evaluation Framework for Multilingual Large Language Models
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Alpaca-Eval (PT)
      type: alpaca_eval_pt
      args:
        num_few_shot: 0
    metrics:
    - type: lc_winrate
      value: 8.8
      name: length controlled winrate
    source:
      url: https://github.com/tatsu-lab/alpaca_eval
      name: AlpacaEval
---

# cnmoro/Tucano-1b1-Instruct-Q8_0-GGUF
This model was converted to GGUF format from [`TucanoBR/Tucano-1b1-Instruct`](https://huggingface.co/TucanoBR/Tucano-1b1-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/TucanoBR/Tucano-1b1-Instruct) for more details on the model.

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

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo cnmoro/Tucano-1b1-Instruct-Q8_0-GGUF --hf-file tucano-1b1-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo cnmoro/Tucano-1b1-Instruct-Q8_0-GGUF --hf-file tucano-1b1-instruct-q8_0.gguf -c 2048
```

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.

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 cnmoro/Tucano-1b1-Instruct-Q8_0-GGUF --hf-file tucano-1b1-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo cnmoro/Tucano-1b1-Instruct-Q8_0-GGUF --hf-file tucano-1b1-instruct-q8_0.gguf -c 2048
```