sentence-transformers How to use Rnfudge/snapd-embedder-v1 with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Rnfudge/snapd-embedder-v1")
sentences = [
"What is the significance of the IPv6 multicast address ff02::1?",
"Felt board for classroom activities",
"In the provided network output, the frequent appearance of `ff020000000000000000000000000001` across various interfaces like `lo`, `eth0`, and `eth1` indicates that these interfaces are correctly configured for basic IPv6 operations. Every active IPv6 interface on a segment must listen for messages sent to `ff02::1` to participate in essential link-local protocols, making its presence a standard and expected entry.",
"Not all customizations are supported across all snapd image types or models. For example, certain customizations might be unsupported for UC20+ or classic models, leading to errors. Additionally, if a gadget snap itself defines `defaults` in its `meta/gadget.yaml`, these can be overridden or complemented by the `Customizations` provided during the `SetupSeed` call, affecting system services like SSH."
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] Unsloth Studio How to use Rnfudge/snapd-embedder-v1 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rnfudge/snapd-embedder-v1 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Rnfudge/snapd-embedder-v1 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rnfudge/snapd-embedder-v1 to start chatting
Load model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="Rnfudge/snapd-embedder-v1",
max_seq_length=2048,
) 
