Instructions to use NBRZ/gpt2-dp-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NBRZ/gpt2-dp-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NBRZ/gpt2-dp-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NBRZ/gpt2-dp-2") model = AutoModelForCausalLM.from_pretrained("NBRZ/gpt2-dp-2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NBRZ/gpt2-dp-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NBRZ/gpt2-dp-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NBRZ/gpt2-dp-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NBRZ/gpt2-dp-2
- SGLang
How to use NBRZ/gpt2-dp-2 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 "NBRZ/gpt2-dp-2" \ --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": "NBRZ/gpt2-dp-2", "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 "NBRZ/gpt2-dp-2" \ --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": "NBRZ/gpt2-dp-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NBRZ/gpt2-dp-2 with Docker Model Runner:
docker model run hf.co/NBRZ/gpt2-dp-2
gpt2-dp-2
This model is a fine-tuned version of gpt2 on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 4.3038
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: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 9
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 6.5574 | 0.53 | 500 | 5.4160 |
| 5.0689 | 1.07 | 1000 | 4.9377 |
| 4.6601 | 1.6 | 1500 | 4.6589 |
| 4.3967 | 2.14 | 2000 | 4.4999 |
| 4.1846 | 2.67 | 2500 | 4.3930 |
| 4.0257 | 3.21 | 3000 | 4.3408 |
| 3.8965 | 3.74 | 3500 | 4.2798 |
| 3.7483 | 4.27 | 4000 | 4.2719 |
| 3.6522 | 4.81 | 4500 | 4.2338 |
| 3.4715 | 5.34 | 5000 | 4.2545 |
| 3.4106 | 5.88 | 5500 | 4.2303 |
| 3.2009 | 6.41 | 6000 | 4.2659 |
| 3.1644 | 6.94 | 6500 | 4.2559 |
| 2.9753 | 7.48 | 7000 | 4.2917 |
| 2.9548 | 8.01 | 7500 | 4.2926 |
| 2.846 | 8.55 | 8000 | 4.3038 |
Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
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