Text Generation
Transformers
Safetensors
PyTorch
English
better_gpt
causal-lm
decoder-only
small-language-model
pretrained
from-scratch
conversational
custom_code
Instructions to use Harikrish2727/BetterGPT-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Harikrish2727/BetterGPT-150M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Harikrish2727/BetterGPT-150M", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Harikrish2727/BetterGPT-150M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Harikrish2727/BetterGPT-150M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Harikrish2727/BetterGPT-150M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harikrish2727/BetterGPT-150M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Harikrish2727/BetterGPT-150M
- SGLang
How to use Harikrish2727/BetterGPT-150M 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 "Harikrish2727/BetterGPT-150M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harikrish2727/BetterGPT-150M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Harikrish2727/BetterGPT-150M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Harikrish2727/BetterGPT-150M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Harikrish2727/BetterGPT-150M with Docker Model Runner:
docker model run hf.co/Harikrish2727/BetterGPT-150M
| import logging | |
| import os | |
| def get_logger(name: str) -> logging.Logger: | |
| """Return a named logger with a StreamHandler, creating handlers only once. | |
| Level defaults to INFO; override with the LOG_LEVEL environment variable. | |
| """ | |
| logger = logging.getLogger(name) | |
| if not logger.handlers: | |
| handler = logging.StreamHandler() | |
| handler.setFormatter( | |
| logging.Formatter( | |
| "%(asctime)s | %(levelname)-8s | %(name)s — %(message)s", | |
| datefmt="%Y-%m-%d %H:%M:%S", | |
| ) | |
| ) | |
| logger.addHandler(handler) | |
| logger.setLevel(os.getenv("LOG_LEVEL", "INFO").upper()) | |
| return logger | |