Text Classification
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
text-embeddings-inference
Instructions to use rd211/SmolLM2-1.7B-Instruct-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rd211/SmolLM2-1.7B-Instruct-RAG with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rd211/SmolLM2-1.7B-Instruct-RAG")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rd211/SmolLM2-1.7B-Instruct-RAG") model = AutoModelForSequenceClassification.from_pretrained("rd211/SmolLM2-1.7B-Instruct-RAG", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from rd211/SmolLM2-1.7B-Instruct-RAG: direct link, hf CLI and curl.
- Browser
- Download file 655 Bytes
-
https://huggingface.co/rd211/SmolLM2-1.7B-Instruct-RAG/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://rd211/SmolLM2-1.7B-Instruct-RAG/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/rd211/SmolLM2-1.7B-Instruct-RAG/resolve/main/special_tokens_map.json
655 Bytes
| { | |
| "additional_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>" | |
| ], | |
| "bos_token": { | |
| "content": "<|im_start|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eos_token": { | |
| "content": "<|im_end|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<|im_end|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |