Text Generation
Transformers
Safetensors
Russian
gpt2
text-adaptation
russian
gpt3
cefr
ruadapt
simplification
text-generation-inference
Instructions to use dwaru/RuAdaptGPT2Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dwaru/RuAdaptGPT2Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dwaru/RuAdaptGPT2Large")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("dwaru/RuAdaptGPT2Large") model = AutoModelForCausalLM.from_pretrained("dwaru/RuAdaptGPT2Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dwaru/RuAdaptGPT2Large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dwaru/RuAdaptGPT2Large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dwaru/RuAdaptGPT2Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dwaru/RuAdaptGPT2Large
- SGLang
How to use dwaru/RuAdaptGPT2Large 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 "dwaru/RuAdaptGPT2Large" \ --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": "dwaru/RuAdaptGPT2Large", "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 "dwaru/RuAdaptGPT2Large" \ --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": "dwaru/RuAdaptGPT2Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dwaru/RuAdaptGPT2Large with Docker Model Runner:
docker model run hf.co/dwaru/RuAdaptGPT2Large
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| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.8382229673093042, | |
| "eval_steps": 500, | |
| "global_step": 1000, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
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| "epoch": 0.4191114836546521, | |
| "grad_norm": 0.951872706413269, | |
| "learning_rate": 4.4761106454316845e-05, | |
| "loss": 1.8004, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.4191114836546521, | |
| "eval_CEFR_f1": 0.3384662, | |
| "eval_loss": 1.1508616209030151, | |
| "eval_rouge1": 0.48, | |
| "eval_rouge2": 0.29292929, | |
| "eval_runtime": 15.0767, | |
| "eval_samples_per_second": 6.633, | |
| "eval_steps_per_second": 0.265, | |
| "step": 500 | |
| }, | |
| { | |
| "epoch": 0.8382229673093042, | |
| "grad_norm": 1.0152556896209717, | |
| "learning_rate": 3.95222129086337e-05, | |
| "loss": 1.2466, | |
| "step": 1000 | |
| }, | |
| { | |
| "epoch": 0.8382229673093042, | |
| "eval_CEFR_f1": 0.32371507, | |
| "eval_loss": 1.0748347043991089, | |
| "eval_rouge1": 0.5, | |
| "eval_rouge2": 0.32323232, | |
| "eval_runtime": 15.1501, | |
| "eval_samples_per_second": 6.601, | |
| "eval_steps_per_second": 0.264, | |
| "step": 1000 | |
| } | |
| ], | |
| "logging_steps": 500, | |
| "max_steps": 4772, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 4, | |
| "save_steps": 500, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 1.9193866223616e+16, | |
| "train_batch_size": 32, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |