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
| { | |
| "alpha_pattern": {}, | |
| "auto_mapping": { | |
| "base_model_class": "LlamaEncoderModel", | |
| "parent_library": "llama_encoder_model.modeling_llama_encoder" | |
| }, | |
| "base_model_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct", | |
| "bias": "none", | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
| "layers_pattern": null, | |
| "layers_to_transform": null, | |
| "loftq_config": {}, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": null, | |
| "peft_type": "LORA", | |
| "r": 16, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
| "k_proj", | |
| "gate_proj", | |
| "down_proj", | |
| "up_proj", | |
| "q_proj", | |
| "o_proj", | |
| "v_proj" | |
| ], | |
| "task_type": null, | |
| "use_rslora": false | |
| } |