Text Classification
Transformers
Safetensors
English
qwen3_5_text
text-generation
decision-model
typed-decisions
one-pass
option-probabilities
Instructions to use thegovind/blink-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thegovind/blink-4b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="thegovind/blink-4b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thegovind/blink-4b") model = AutoModelForCausalLM.from_pretrained("thegovind/blink-4b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from thegovind/blink-4b: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/thegovind/blink-4b/resolve/5fbc1e9cd9912d912c7cf412b7da29937ca20f30/tokenizer.json
- Command line
-
hf download hf://thegovind/blink-4b@5fbc1e9cd9912d912c7cf412b7da29937ca20f30/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/thegovind/blink-4b/resolve/5fbc1e9cd9912d912c7cf412b7da29937ca20f30/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.