Feature Extraction
sentence-transformers
Safetensors
Transformers
English
mistral
bnb-my-repo
mteb
Eval Results (legacy)
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use ashercn97/Linq-Embed-Mistral-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use ashercn97/Linq-Embed-Mistral-bnb-4bit with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("ashercn97/Linq-Embed-Mistral-bnb-4bit") 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] - Transformers
How to use ashercn97/Linq-Embed-Mistral-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ashercn97/Linq-Embed-Mistral-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("ashercn97/Linq-Embed-Mistral-bnb-4bit") model = AutoModelForMultimodalLM.from_pretrained("ashercn97/Linq-Embed-Mistral-bnb-4bit") - Notebooks
- Google Colab
- Kaggle
Create 1_Pooling/config.json
Browse files- 1_Pooling/config.json +10 -0
1_Pooling/config.json
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{
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"word_embedding_dimension": 4096,
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"pooling_mode_cls_token": false,
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"pooling_mode_mean_tokens": false,
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"pooling_mode_max_tokens": false,
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"pooling_mode_mean_sqrt_len_tokens": false,
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"pooling_mode_weightedmean_tokens": false,
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"pooling_mode_lasttoken": true,
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"include_prompt": true
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}
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