Instructions to use amogaddy/GenerAI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amogaddy/GenerAI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="amogaddy/GenerAI")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("amogaddy/GenerAI", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use amogaddy/GenerAI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "amogaddy/GenerAI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "amogaddy/GenerAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/amogaddy/GenerAI
- SGLang
How to use amogaddy/GenerAI 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 "amogaddy/GenerAI" \ --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": "amogaddy/GenerAI", "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 "amogaddy/GenerAI" \ --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": "amogaddy/GenerAI", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use amogaddy/GenerAI with Docker Model Runner:
docker model run hf.co/amogaddy/GenerAI
File size: 2,384 Bytes
37ffa75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | import logging
import traceback
import sys
from enum import Enum
# ββ Logging setup βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
LOG_FORMAT = "[%(asctime)s] %(levelname)-8s %(name)s β %(message)s"
DATE_FORMAT = "%H:%M:%S"
logging.basicConfig(
level=logging.DEBUG,
format=LOG_FORMAT,
datefmt=DATE_FORMAT,
handlers=[
logging.StreamHandler(sys.stdout),
logging.FileHandler("generai.log", encoding="utf-8"),
],
)
def get_logger(name: str) -> logging.Logger:
return logging.getLogger(name)
# ββ Error categories βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
class ErrorCode(Enum):
# DB / Knowledge Base
KB_INIT_FAILED = "KB-001"
KB_SEARCH_FAILED = "KB-002"
KB_WRITE_FAILED = "KB-003"
KB_REINFORCE_FAILED = "KB-004"
KB_SEED_FAILED = "KB-005"
# Scraper / Web
WEB_SEARCH_FAILED = "WEB-001"
WEB_FETCH_FAILED = "WEB-002"
WEB_EXTRACT_FAILED = "WEB-003"
WEB_NO_RESULTS = "WEB-004"
# Brain
BRAIN_ASK_FAILED = "BRN-001"
BRAIN_ASYNC_FAILED = "BRN-002"
# UI
UI_INPUT_EMPTY = "UI-001"
UI_HANDLER_FAILED = "UI-002"
class GenerAIError(Exception):
"""Base exception with error code and user-friendly message."""
def __init__(self, code: ErrorCode, detail: str, cause: Exception | None = None):
self.code = code
self.detail = detail
self.cause = cause
super().__init__(f"[{code.value}] {detail}")
def user_message(self) -> str:
cause_str = f"\n> Causa: `{type(self.cause).__name__}: {self.cause}`" if self.cause else ""
return f"β οΈ **Errore {self.code.value}** β {self.detail}{cause_str}"
def log(self, logger: logging.Logger):
logger.error("[%s] %s", self.code.value, self.detail)
if self.cause:
logger.debug("Traceback originale:\n%s", "".join(traceback.format_exception(type(self.cause), self.cause, self.cause.__traceback__)))
def fmt_exc(e: Exception) -> str:
"""One-line summary of an exception for log messages."""
return f"{type(e).__name__}: {e}"
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