Instructions to use guidoivetta/peppa_pig with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use guidoivetta/peppa_pig with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="guidoivetta/peppa_pig")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("guidoivetta/peppa_pig") model = AutoModelForCausalLM.from_pretrained("guidoivetta/peppa_pig") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use guidoivetta/peppa_pig with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "guidoivetta/peppa_pig" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "guidoivetta/peppa_pig", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/guidoivetta/peppa_pig
- SGLang
How to use guidoivetta/peppa_pig 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 "guidoivetta/peppa_pig" \ --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": "guidoivetta/peppa_pig", "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 "guidoivetta/peppa_pig" \ --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": "guidoivetta/peppa_pig", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use guidoivetta/peppa_pig with Docker Model Runner:
docker model run hf.co/guidoivetta/peppa_pig
peppa_pig
This model is a fine-tuned version of DeepESP/gpt2-spanish on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 2.3870
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: 2e-05
- 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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.6125 | 1.0 | 28 | 2.6842 |
| 2.3335 | 2.0 | 56 | 2.5216 |
| 2.1436 | 3.0 | 84 | 2.4615 |
| 1.9946 | 4.0 | 112 | 2.4247 |
| 1.9846 | 5.0 | 140 | 2.4094 |
| 1.8404 | 6.0 | 168 | 2.3987 |
| 1.7489 | 7.0 | 196 | 2.3924 |
| 1.7656 | 8.0 | 224 | 2.3894 |
| 1.744 | 9.0 | 252 | 2.3880 |
| 1.6455 | 10.0 | 280 | 2.3870 |
Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3
- Downloads last month
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Model tree for guidoivetta/peppa_pig
Base model
DeepESP/gpt2-spanish