Text Generation
Transformers
PyTorch
TensorBoard
Russian
gpt2
Generated from Trainer
text-generation-inference
Instructions to use vldsavelyev/murakami_rugpt3small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vldsavelyev/murakami_rugpt3small with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="vldsavelyev/murakami_rugpt3small")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("vldsavelyev/murakami_rugpt3small") model = AutoModelForCausalLM.from_pretrained("vldsavelyev/murakami_rugpt3small", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use vldsavelyev/murakami_rugpt3small with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vldsavelyev/murakami_rugpt3small" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vldsavelyev/murakami_rugpt3small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vldsavelyev/murakami_rugpt3small
- SGLang
How to use vldsavelyev/murakami_rugpt3small 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 "vldsavelyev/murakami_rugpt3small" \ --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": "vldsavelyev/murakami_rugpt3small", "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 "vldsavelyev/murakami_rugpt3small" \ --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": "vldsavelyev/murakami_rugpt3small", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vldsavelyev/murakami_rugpt3small with Docker Model Runner:
docker model run hf.co/vldsavelyev/murakami_rugpt3small
Commit History
-
Upload tokenizer
95b3c55 -
Upload config
ce177c4 -
Upload tokenizer
5b45e17 -
Upload config
59b98ce -
Upload tokenizer
c99d71b -
Upload tokenizer
9338889 -
Upload tokenizer
67165a8 -
Upload config
f66f8bc -
Upload config
c65a978 -
Upload config
75d669e -
Upload config
6523884 -
Upload tokenizer
c29b35e -
Upload config
3f68ff6 -
Update README
f7e5ef1 -
Update model card
ba810ec
-
3 epochs
e520818
-
Fix readme
e7e38bd
-
update model card README.md
970e930 -
Model save
28ffa63 -
Model save
193a5bd -
Upload model
9e8d7ec -
Training in progress, step 10000
3f03c26 -
Training in progress, step 5000
0b2201e -
Training in progress, step 2000
3ad2181 -
Training in progress, step 1000
e689f29 -
update model card README.md
4376dfe -
Model save
30a123f -
initial commit
fd61305