Instructions to use eudesfilho/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use eudesfilho/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="eudesfilho/results")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("eudesfilho/results") model = AutoModelForCausalLM.from_pretrained("eudesfilho/results") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use eudesfilho/results with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "eudesfilho/results" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "eudesfilho/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/eudesfilho/results
- SGLang
How to use eudesfilho/results 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 "eudesfilho/results" \ --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": "eudesfilho/results", "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 "eudesfilho/results" \ --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": "eudesfilho/results", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use eudesfilho/results with Docker Model Runner:
docker model run hf.co/eudesfilho/results
results
This model is a fine-tuned version of pierreguillou/gpt2-small-portuguese on the None dataset. It achieves the following results on the evaluation set:
- Loss: 4.1675
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 4.0373 | 1.0 | 38 | 4.1597 |
| 3.8691 | 2.0 | 76 | 4.1378 |
| 3.8037 | 3.0 | 114 | 4.1324 |
| 3.6804 | 4.0 | 152 | 4.1340 |
| 3.5658 | 5.0 | 190 | 4.1411 |
| 3.4923 | 6.0 | 228 | 4.1479 |
| 3.3789 | 7.0 | 266 | 4.1537 |
| 3.4002 | 8.0 | 304 | 4.1621 |
| 3.3624 | 9.0 | 342 | 4.1654 |
| 3.3 | 10.0 | 380 | 4.1675 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
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Model tree for eudesfilho/results
Base model
pierreguillou/gpt2-small-portuguese