Feature Extraction
Transformers
Safetensors
PEFT
English
llm2vec
embedding
sentence-similarity
text-encoder
llama3
kimodo
quantized
bitsandbytes
nf4
4-bit precision
lora
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 AutoModel model = AutoModel.from_pretrained("matbee/kimodo-llm2vec-nf4", dtype="auto") - PEFT
How to use matbee/kimodo-llm2vec-nf4 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|begin_of_text|>", | |
| "clean_up_tokenization_spaces": true, | |
| "eos_token": "<|eot_id|>", | |
| "is_local": true, | |
| "model_input_names": [ | |
| "input_ids", | |
| "attention_mask" | |
| ], | |
| "model_max_length": 1000000000000000019884624838656, | |
| "tokenizer_class": "TokenizersBackend" | |
| } | |