Feature Extraction
Transformers
Safetensors
English
llama
text-generation
text-generation-inference
unsloth
phi-4
information-extraction
text-embeddings-inference
4-bit precision
bitsandbytes
Instructions to use RahulPi/Email_Text_Formatter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RahulPi/Email_Text_Formatter with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RahulPi/Email_Text_Formatter")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RahulPi/Email_Text_Formatter") model = AutoModelForCausalLM.from_pretrained("RahulPi/Email_Text_Formatter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
File size: 570 Bytes
7a9024b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"bos_token": {
"content": "<|endoftext|>",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false
},
"eos_token": {
"content": "<|im_end|>",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false
},
"pad_token": {
"content": "<|dummy_87|>",
"lstrip": true,
"normalized": false,
"rstrip": true,
"single_word": false
},
"unk_token": {
"content": "�",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
|