Text Classification
Transformers
Safetensors
English
Turkish
German
qwen3_5
image-text-to-text
decision-model
calibration
conformal-prediction
uncertainty
reasoning
routing
triage
jev
typesafe
qwen3.5
english
small-language-model
local-llm
on-device
english-llm
Instructions to use mertkayacs/Deem-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mertkayacs/Deem-4B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="mertkayacs/Deem-4B")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mertkayacs/Deem-4B") model = AutoModelForMultimodalLM.from_pretrained("mertkayacs/Deem-4B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from mertkayacs/Deem-4B: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/mertkayacs/Deem-4B/resolve/71c2de7fe3067ab36989987aba549a7a9c185403/tokenizer.json
- Command line
-
hf download hf://mertkayacs/Deem-4B@71c2de7fe3067ab36989987aba549a7a9c185403/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/mertkayacs/Deem-4B/resolve/71c2de7fe3067ab36989987aba549a7a9c185403/tokenizer.json
20 MB
- Xet hash:
- df2446dd51fd4eb9bb6ebee468ea0176876cb0d4171ba0054b3614c3da817486
- Size of remote file:
- 20 MB
- SHA256:
- 970a1af62fb366b6e785135b4ac94323467753d6539c9e27044d4b22f6ffbea3
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