Text Classification
Transformers
Safetensors
PEFT
English
retrievalrouter
feature-extraction
retrieval
document-retrieval
information-retrieval
routing
RAG
query-routing
late-interaction
lora
custom_code
Instructions to use emrekuruu/RetrievalRouter-lambda-l50 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use emrekuruu/RetrievalRouter-lambda-l50 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="emrekuruu/RetrievalRouter-lambda-l50", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("emrekuruu/RetrievalRouter-lambda-l50", trust_remote_code=True, device_map="auto") - PEFT
How to use emrekuruu/RetrievalRouter-lambda-l50 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
| { | |
| "add_prefix_space": false, | |
| "backend": "tokenizers", | |
| "bos_token": null, | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|endoftext|>", | |
| "errors": "replace", | |
| "extra_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>" | |
| ], | |
| "is_local": true, | |
| "local_files_only": false, | |
| "max_length": 128, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "padding_side": "right", | |
| "split_special_tokens": false, | |
| "stride": 0, | |
| "tokenizer_class": "Qwen2Tokenizer", | |
| "truncation_side": "right", | |
| "truncation_strategy": "longest_first", | |
| "unk_token": null | |
| } | |