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")# pip install -U transformers accelerate # 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
Upload errors.py with huggingface_hub
Browse files
errors.py
ADDED
|
@@ -0,0 +1,69 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import logging
|
| 2 |
+
import traceback
|
| 3 |
+
import sys
|
| 4 |
+
from enum import Enum
|
| 5 |
+
|
| 6 |
+
# ββ Logging setup βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
|
| 7 |
+
|
| 8 |
+
LOG_FORMAT = "[%(asctime)s] %(levelname)-8s %(name)s β %(message)s"
|
| 9 |
+
DATE_FORMAT = "%H:%M:%S"
|
| 10 |
+
|
| 11 |
+
logging.basicConfig(
|
| 12 |
+
level=logging.DEBUG,
|
| 13 |
+
format=LOG_FORMAT,
|
| 14 |
+
datefmt=DATE_FORMAT,
|
| 15 |
+
handlers=[
|
| 16 |
+
logging.StreamHandler(sys.stdout),
|
| 17 |
+
logging.FileHandler("generai.log", encoding="utf-8"),
|
| 18 |
+
],
|
| 19 |
+
)
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
def get_logger(name: str) -> logging.Logger:
|
| 23 |
+
return logging.getLogger(name)
|
| 24 |
+
|
| 25 |
+
|
| 26 |
+
# ββ Error categories βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
|
| 27 |
+
|
| 28 |
+
class ErrorCode(Enum):
|
| 29 |
+
# DB / Knowledge Base
|
| 30 |
+
KB_INIT_FAILED = "KB-001"
|
| 31 |
+
KB_SEARCH_FAILED = "KB-002"
|
| 32 |
+
KB_WRITE_FAILED = "KB-003"
|
| 33 |
+
KB_REINFORCE_FAILED = "KB-004"
|
| 34 |
+
KB_SEED_FAILED = "KB-005"
|
| 35 |
+
# Scraper / Web
|
| 36 |
+
WEB_SEARCH_FAILED = "WEB-001"
|
| 37 |
+
WEB_FETCH_FAILED = "WEB-002"
|
| 38 |
+
WEB_EXTRACT_FAILED = "WEB-003"
|
| 39 |
+
WEB_NO_RESULTS = "WEB-004"
|
| 40 |
+
# Brain
|
| 41 |
+
BRAIN_ASK_FAILED = "BRN-001"
|
| 42 |
+
BRAIN_ASYNC_FAILED = "BRN-002"
|
| 43 |
+
# UI
|
| 44 |
+
UI_INPUT_EMPTY = "UI-001"
|
| 45 |
+
UI_HANDLER_FAILED = "UI-002"
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
class GenerAIError(Exception):
|
| 49 |
+
"""Base exception with error code and user-friendly message."""
|
| 50 |
+
|
| 51 |
+
def __init__(self, code: ErrorCode, detail: str, cause: Exception | None = None):
|
| 52 |
+
self.code = code
|
| 53 |
+
self.detail = detail
|
| 54 |
+
self.cause = cause
|
| 55 |
+
super().__init__(f"[{code.value}] {detail}")
|
| 56 |
+
|
| 57 |
+
def user_message(self) -> str:
|
| 58 |
+
cause_str = f"\n> Causa: `{type(self.cause).__name__}: {self.cause}`" if self.cause else ""
|
| 59 |
+
return f"β οΈ **Errore {self.code.value}** β {self.detail}{cause_str}"
|
| 60 |
+
|
| 61 |
+
def log(self, logger: logging.Logger):
|
| 62 |
+
logger.error("[%s] %s", self.code.value, self.detail)
|
| 63 |
+
if self.cause:
|
| 64 |
+
logger.debug("Traceback originale:\n%s", "".join(traceback.format_exception(type(self.cause), self.cause, self.cause.__traceback__)))
|
| 65 |
+
|
| 66 |
+
|
| 67 |
+
def fmt_exc(e: Exception) -> str:
|
| 68 |
+
"""One-line summary of an exception for log messages."""
|
| 69 |
+
return f"{type(e).__name__}: {e}"
|