Feature Extraction
Transformers
Safetensors
PEFT
English
llama
llm2vec
embedding
sentence-similarity
text-encoder
llama3
kimodo
quantized
bitsandbytes
nf4
4-bit precision
lora
text-embeddings-inference
Instructions to use matbee/kimodo-llm2vec-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use matbee/kimodo-llm2vec-nf4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="matbee/kimodo-llm2vec-nf4")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("matbee/kimodo-llm2vec-nf4") model = AutoModel.from_pretrained("matbee/kimodo-llm2vec-nf4") - PEFT
How to use matbee/kimodo-llm2vec-nf4 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d1764001543899106a3168f0d55a6a527fadce55b617f89242903ae4ace0ef8d
- Size of remote file:
- 17.2 MB
- SHA256:
- 3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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