Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
decider
systemone
jev
decision-model
qwen3.5
post-train
text-to-typed-output
structured-output
classification
tool-calling
routing
moderation
custom-code
Eval Results (legacy)
Instructions to use Jeetu456/decider-2b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeetu456/decider-2b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Jeetu456/decider-2b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Jeetu456/decider-2b") model = AutoModelForCausalLM.from_pretrained("Jeetu456/decider-2b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download deploy/runpod-serverless/handler.py from Jeetu456/decider-2b: direct link, hf CLI and curl.
- Browser
- Download file 1.07 kB
-
https://huggingface.co/Jeetu456/decider-2b/resolve/main/deploy/runpod-serverless/handler.py
- Command line
-
hf download hf://Jeetu456/decider-2b/deploy/runpod-serverless/handler.py
-
curl -L -o handler.py https://huggingface.co/Jeetu456/decider-2b/resolve/main/deploy/runpod-serverless/handler.py
1.07 kB
| """RunPod Serverless worker: one Jev/SystemOne request (or a list) per job, billed per second. | |
| Job input: {"input": <request>} or {"input": {"requests": [<request>, ...]}}. | |
| The model loads once per worker from DECIDER_MODEL (repo id or folder). | |
| """ | |
| import os | |
| import sys | |
| import runpod | |
| import torch | |
| from huggingface_hub import snapshot_download | |
| MODEL = os.environ.get("DECIDER_MODEL", "Jeetu456/decider-2b") | |
| path = MODEL if os.path.isdir(MODEL) else snapshot_download(MODEL) | |
| sys.path.insert(0, path) | |
| from decider_model import SchemaError, load_release_model, systemone # noqa: E402 | |
| model = load_release_model(path, device="cuda" if torch.cuda.is_available() else "cpu") | |
| def handler(job): | |
| request = job["input"] | |
| try: | |
| if isinstance(request, dict) and "requests" in request: | |
| return [systemone(model, r) for r in request["requests"]] | |
| return systemone(model, request) | |
| except (SchemaError, ValueError) as error: | |
| return {"error": str(error)} | |
| if __name__ == "__main__": | |
| runpod.serverless.start({"handler": handler}) | |