Text Generation
Transformers
PyTorch
JAX
Safetensors
Portuguese
llama
text-generation-inference
Eval Results (legacy)
Instructions to use TucanoBR/Tucano-2b4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TucanoBR/Tucano-2b4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TucanoBR/Tucano-2b4")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("TucanoBR/Tucano-2b4") model = AutoModelForMultimodalLM.from_pretrained("TucanoBR/Tucano-2b4") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TucanoBR/Tucano-2b4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TucanoBR/Tucano-2b4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TucanoBR/Tucano-2b4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TucanoBR/Tucano-2b4
- SGLang
How to use TucanoBR/Tucano-2b4 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "TucanoBR/Tucano-2b4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TucanoBR/Tucano-2b4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "TucanoBR/Tucano-2b4" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TucanoBR/Tucano-2b4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TucanoBR/Tucano-2b4 with Docker Model Runner:
docker model run hf.co/TucanoBR/Tucano-2b4
Upload results-multilingual.json with huggingface_hub
Browse files- results-multilingual.json +37 -0
results-multilingual.json
ADDED
|
@@ -0,0 +1,37 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"results": {
|
| 3 |
+
"arc_pt": {
|
| 4 |
+
"acc": 0.26666666666666666,
|
| 5 |
+
"acc_stderr": 0.012933850109759568,
|
| 6 |
+
"acc_norm": 0.30256410256410254,
|
| 7 |
+
"acc_norm_stderr": 0.013435492568854205
|
| 8 |
+
},
|
| 9 |
+
"hellaswag_pt": {
|
| 10 |
+
"acc": 0.37772239679271863,
|
| 11 |
+
"acc_stderr": 0.005046899546439457,
|
| 12 |
+
"acc_norm": 0.47014844511864773,
|
| 13 |
+
"acc_norm_stderr": 0.00519566110400487
|
| 14 |
+
},
|
| 15 |
+
"truthfulqa_pt": {
|
| 16 |
+
"mc1": 0.24111675126903553,
|
| 17 |
+
"mc1_stderr": 0.015248032494426427,
|
| 18 |
+
"mc2": 0.3911004265246538,
|
| 19 |
+
"mc2_stderr": 0.014560723432804852
|
| 20 |
+
}
|
| 21 |
+
},
|
| 22 |
+
"versions": {
|
| 23 |
+
"arc_pt": 0,
|
| 24 |
+
"hellaswag_pt": 1,
|
| 25 |
+
"truthfulqa_pt": 1
|
| 26 |
+
},
|
| 27 |
+
"config": {
|
| 28 |
+
"model": "hf-auto",
|
| 29 |
+
"model_args": "pretrained=/lustre/mlnvme/data/asen_hpc-mula/checkpoints-llama/slurm_job_17049106/step_1960000",
|
| 30 |
+
"batch_size": 1,
|
| 31 |
+
"device": "cuda:0",
|
| 32 |
+
"no_cache": false,
|
| 33 |
+
"limit": null,
|
| 34 |
+
"bootstrap_iters": 100000,
|
| 35 |
+
"description_dict": {}
|
| 36 |
+
}
|
| 37 |
+
}
|