metadata
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 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 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:
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 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