Instructions to use xiulinyang/GPT2_AR_200 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiulinyang/GPT2_AR_200 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="xiulinyang/GPT2_AR_200")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("xiulinyang/GPT2_AR_200") model = AutoModelForCausalLM.from_pretrained("xiulinyang/GPT2_AR_200", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use xiulinyang/GPT2_AR_200 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "xiulinyang/GPT2_AR_200" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "xiulinyang/GPT2_AR_200", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/xiulinyang/GPT2_AR_200
- SGLang
How to use xiulinyang/GPT2_AR_200 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 "xiulinyang/GPT2_AR_200" \ --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": "xiulinyang/GPT2_AR_200", "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 "xiulinyang/GPT2_AR_200" \ --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": "xiulinyang/GPT2_AR_200", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use xiulinyang/GPT2_AR_200 with Docker Model Runner:
docker model run hf.co/xiulinyang/GPT2_AR_200
AR_200_41
This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.5234
- Accuracy: 0.5250
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: 0.0006
- train_batch_size: 32
- eval_batch_size: 32
- seed: 41
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.5298 | 1.0 | 2845 | 1.9969 | 0.3939 |
| 0.4512 | 2.0 | 5690 | 1.8217 | 0.4384 |
| 0.4173 | 3.0 | 8535 | 1.7295 | 0.4640 |
| 0.3977 | 4.0 | 11380 | 1.6786 | 0.4796 |
| 0.3841 | 5.0 | 14225 | 1.6362 | 0.4906 |
| 0.3747 | 6.0 | 17070 | 1.6065 | 0.4991 |
| 0.3677 | 7.0 | 19915 | 1.5772 | 0.5071 |
| 0.3586 | 8.0 | 22760 | 1.5534 | 0.5143 |
| 0.3504 | 9.0 | 25605 | 1.5324 | 0.5215 |
| 0.3437 | 10.0 | 28450 | 1.5234 | 0.5250 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
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