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
Card: official JevBench column says no official score published
Browse files
README.md
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| Model | Public-items proxy | Public hard (111) | Hard ECE | Probability TVD | Official JevBench v1.4 |
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| **blink-4b** | 76.5 | 80/111 | 0.067 | 0.226 |
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| JevK5 v0.2.0 | 76.1 | 79/111 (own runtime: 82/111) | 0.068 | 0.220 | 62.0 |
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| Jev 1.13.0 | — | — | — | — | 63.3 |
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| Model | Public-items proxy | Public hard (111) | Hard ECE | Probability TVD | Official JevBench v1.4 |
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| **blink-4b** | 76.5 | 80/111 | 0.067 | 0.226 | no official score published |
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| JevK5 v0.2.0 | 76.1 | 79/111 (own runtime: 82/111) | 0.068 | 0.220 | 62.0 |
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| Jev 1.13.0 | — | — | — | — | 63.3 |
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