Feature Extraction
sentence-transformers
PyTorch
Safetensors
Transformers
English
mistral
mteb
Eval Results (legacy)
Eval Results
text-embeddings-inference
Instructions to use intfloat/e5-mistral-7b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use intfloat/e5-mistral-7b-instruct with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-mistral-7b-instruct") 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 intfloat/e5-mistral-7b-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="intfloat/e5-mistral-7b-instruct")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("intfloat/e5-mistral-7b-instruct") model = AutoModel.from_pretrained("intfloat/e5-mistral-7b-instruct") - Inference
- Notebooks
- Google Colab
- Kaggle
How much GPU memory is required for 32k context embedding?
#32
by Labmem009 - opened
I tried to use this model to get embedding of long text, but I failed many times with 6*A100 and DP for OOM. Is there any suggestion to allocate memory for long text?
For 32k context, it needs to run on an 80GB A100 GPU with float16 / bfloat16 and FlashAttention enabled, also the batch size needs to be reduced to 1.