Feature Extraction
Transformers
Safetensors
sentence-transformers
English
qwen3
embeddings
reward-model
verifier
agents
calibration
lora
text-embeddings-inference
Instructions to use spandyie/makalu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spandyie/makalu with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="spandyie/makalu")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spandyie/makalu") model = AutoModel.from_pretrained("spandyie/makalu", device_map="auto") - sentence-transformers
How to use spandyie/makalu with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("spandyie/makalu") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Download 2_Normalize/config.json from spandyie/makalu: direct link, hf CLI and curl.
- Browser
- Download file 3 Bytes
-
https://huggingface.co/spandyie/makalu/resolve/main/2_Normalize/config.json
- Command line
-
hf download hf://spandyie/makalu/2_Normalize/config.json
-
curl -L -o config.json https://huggingface.co/spandyie/makalu/resolve/main/2_Normalize/config.json
3 Bytes
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