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
Download chat_template.jinja from RahulPi/Email_Text_Formatter: direct link, hf CLI and curl.
- Browser
- Download file 462 Bytes
-
https://huggingface.co/RahulPi/Email_Text_Formatter/resolve/main/chat_template.jinja
- Command line
-
hf download hf://RahulPi/Email_Text_Formatter/chat_template.jinja
-
curl -L -o chat_template.jinja https://huggingface.co/RahulPi/Email_Text_Formatter/resolve/main/chat_template.jinja
462 Bytes
| {% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %} |