Sentence Similarity
sentence-transformers
PyTorch
English
bert
feature-extraction
mteb
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use Hum-Works/lodestone-base-4096-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Hum-Works/lodestone-base-4096-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Hum-Works/lodestone-base-4096-v1", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Mosaic_model.ipynb mentioned in the notebook is missing
#3
by alex-gptzero - opened
This was described in Replication.txt
Is it still necessary for preparing the model on our own GPU?
Running the model should work on a CPU, but further training or replication requires a GPU (specs noted in Replication.txt)
Hey @alex-gptzero , in our attempt to cleanup a bit of our chaotic files when we shared the model we forgot to include this notebook.
I went ahead and pulled that into a script load_mosaic.py instead of adding the notebook and updated the Replication.txt.
Trying to keep it straightforward and simple.
-Dylan
dylanAtHum changed discussion status to closed