Sentence Similarity
Transformers
Safetensors
text-embedding
embeddings
information-retrieval
beir
text-classification
language-model
text-clustering
text-semantic-similarity
text-evaluation
text-reranking
feature-extraction
Sentence Similarity
natural_questions
ms_marco
fever
hotpot_qa
mteb
advbenchir
bright
Instructions to use McGill-NLP/LLM2Vec-Gen-Qwen3-17B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use McGill-NLP/LLM2Vec-Gen-Qwen3-17B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("McGill-NLP/LLM2Vec-Gen-Qwen3-17B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from McGill-NLP/LLM2Vec-Gen-Qwen3-17B: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/McGill-NLP/LLM2Vec-Gen-Qwen3-17B/resolve/main/tokenizer.json
- Command line
-
hf download hf://McGill-NLP/LLM2Vec-Gen-Qwen3-17B/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/McGill-NLP/LLM2Vec-Gen-Qwen3-17B/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 1bbb6e3ebdf92f01572e3097d00bfc642c43ba8f447e2e3a4030fa64afd6de2b
- Size of remote file:
- 11.4 MB
- SHA256:
- 463fa1e93fee8fb2f786cacb0df393c35c48d3269be9cc017ab83319a07348c2
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