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")# pip install -U transformers accelerate # 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 graft_keys.py from thegovind/blink-4b: direct link, hf CLI and curl.
- Browser
- Download file 369 Bytes
-
https://huggingface.co/thegovind/blink-4b/resolve/f7f0e343e5f327d93a71e4cf2f0c56fd687c7665/graft_keys.py
- Command line
-
hf download hf://thegovind/blink-4b@f7f0e343e5f327d93a71e4cf2f0c56fd687c7665/graft_keys.py
-
curl -L -o graft_keys.py https://huggingface.co/thegovind/blink-4b/resolve/f7f0e343e5f327d93a71e4cf2f0c56fd687c7665/graft_keys.py
369 Bytes
| """Map merged text weights to their matching vision-language parent.""" | |
| def text_to_parent(key: str) -> str: | |
| if key == "lm_head.weight" or key.startswith("model.language_model."): | |
| return key | |
| if key.startswith("model."): | |
| return "model.language_model." + key[len("model."):] | |
| raise ValueError(f"unexpected key in the text checkpoint: {key}") | |