Feature Extraction
Transformers
Safetensors
qwen2
bnb-my-repo
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use bnb-community/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bnb-community/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="bnb-community/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("bnb-community/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("bnb-community/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7c4a58d2a4f0738c6d57fa0eb0482de5d337e2a0261f8f99fd79248f4acd408
- Size of remote file:
- 2.77 GB
- SHA256:
- d16d282b02b6a0675051fdebbaeb885a3cce28258542230463b21adfac7e1871
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.