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
File size: 471 Bytes
c9fb27c | 1 2 3 4 5 6 7 8 | {
"quantization": "nf4",
"source_base": "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp",
"source_supervised": "McGill-NLP/LLM2Vec-Meta-Llama-3-8B-Instruct-mntp-supervised",
"torch": "2.6.0+cu124",
"cuda": "12.4",
"method": "Snapshot-downloaded base WITHOUT adapter, bnb-quantized via LlamaBiModel.from_pretrained, save_pretrained. MNTP adapter files copied into base/ so reload mirrors Hub flow. Supervised adapter in sibling dir for LLM2VEC_LOCAL_PEFT."
} |