Instructions to use Roaoch/CyberClassic-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Roaoch/CyberClassic-Generator with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Roaoch/CyberClassic-Generator")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Roaoch/CyberClassic-Generator") model = AutoModelForCausalLM.from_pretrained("Roaoch/CyberClassic-Generator", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Roaoch/CyberClassic-Generator with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Roaoch/CyberClassic-Generator" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Roaoch/CyberClassic-Generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Roaoch/CyberClassic-Generator
- SGLang
How to use Roaoch/CyberClassic-Generator 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 "Roaoch/CyberClassic-Generator" \ --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": "Roaoch/CyberClassic-Generator", "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 "Roaoch/CyberClassic-Generator" \ --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": "Roaoch/CyberClassic-Generator", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Roaoch/CyberClassic-Generator with Docker Model Runner:
docker model run hf.co/Roaoch/CyberClassic-Generator
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Download README.md from Roaoch/CyberClassic-Generator: direct link, hf CLI and curl.
- Browser
- Download file 1.33 kB
-
https://huggingface.co/Roaoch/CyberClassic-Generator/resolve/main/README.md
- Command line
-
hf download hf://Roaoch/CyberClassic-Generator/README.md
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curl -L -o README.md https://huggingface.co/Roaoch/CyberClassic-Generator/resolve/main/README.md
1.33 kB
| license: mit | |
| language: | |
| - ru | |
| metrics: | |
| - perplexity | |
| - bleu | |
| - rouge | |
| library_name: transformers | |
| pipeline_tag: text-generation | |
| This text generator is based on OpenAI GPT2 model from HuggingFace | |
| Base model went through two step of learning | |
| ## First - Finetining of base model | |
| On this step model is finetuned on dataset of single sentence from the texts of Dostovesky F.M. | |
| Training parameters: | |
| * Epoch = 10 | |
| * Learning Rate = 1e-3 | |
| * Optimizer = AdamW | |
| * Scheduler = OneCycleLR | |
| * Training env = PyTorch | |
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| ## Second - RL | |
| On this step finetuned model went trough reinforcement learning pipline with TRL library. | |
| Training parameters: | |
| * Epoch = 30 | |
| * Trainer = PPO | |
| * Query texts = first 100 texts from dataset, trimmed by first 3 words | |
| * Reward = score from [binary classifier](https://huggingface.co/Roaoch/CyberClassic-Discriminator) multiplied by 10 | |
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