CyberClassic
Collection
This is a models for telegram bot - https://t.me/cyber_classic_bot • 4 items • Updated • 1
How to use from
SGLangUse 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
}'Quick Links
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
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 multiplied by 10
- Downloads last month
- 8
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 }'