Text Generation
Transformers
TensorBoard
Safetensors
gpt2
Generated from Trainer
text-generation-inference
Instructions to use joaohonorato/PLN_TS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use joaohonorato/PLN_TS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="joaohonorato/PLN_TS")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("joaohonorato/PLN_TS") model = AutoModelForCausalLM.from_pretrained("joaohonorato/PLN_TS", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use joaohonorato/PLN_TS with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "joaohonorato/PLN_TS" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "joaohonorato/PLN_TS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/joaohonorato/PLN_TS
- SGLang
How to use joaohonorato/PLN_TS 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 "joaohonorato/PLN_TS" \ --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": "joaohonorato/PLN_TS", "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 "joaohonorato/PLN_TS" \ --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": "joaohonorato/PLN_TS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use joaohonorato/PLN_TS with Docker Model Runner:
docker model run hf.co/joaohonorato/PLN_TS
PLN_TS
This model is a fine-tuned version of openai-community/gpt2-medium on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 10.7341
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: 0.002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 91 | 4.6308 |
| No log | 2.0 | 182 | 5.1050 |
| No log | 3.0 | 273 | 5.5102 |
| No log | 4.0 | 364 | 6.2532 |
| No log | 5.0 | 455 | 6.6069 |
| 1.1628 | 6.0 | 546 | 7.0238 |
| 1.1628 | 7.0 | 637 | 7.1553 |
| 1.1628 | 8.0 | 728 | 7.7253 |
| 1.1628 | 9.0 | 819 | 8.2397 |
| 1.1628 | 10.0 | 910 | 8.9225 |
| 0.1611 | 11.0 | 1001 | 9.3999 |
| 0.1611 | 12.0 | 1092 | 9.8062 |
| 0.1611 | 13.0 | 1183 | 10.1804 |
| 0.1611 | 14.0 | 1274 | 10.5743 |
| 0.1611 | 15.0 | 1365 | 10.7341 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for joaohonorato/PLN_TS
Base model
openai-community/gpt2-medium
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "joaohonorato/PLN_TS"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "joaohonorato/PLN_TS", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'