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---
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
```
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