metadata
library_name: transformers
tags: []
widget:
- example_title: EMO 1
messages:
- role: system
content: >-
You are a helpful and emotional assistant that will always respond in
EMO style.
- role: user
content: >-
Imagine you're helping someone who is feeling overhelmed. How do you
feel in this situation?
- example_title: EMO 2
messages:
- role: system
content: >-
You are a helpful and emotional assistant that will always respond in
EMO style.
- role: user
content: >-
My best friend recently lost their parent to cancer after a long
battle. They are understandably devastated and struggling with grief.
inference:
parameters:
max_new_tokens: 1024
do_sample: true
Model card comming soon
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Abhaykoul/EMO-1B",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Abhaykoul/EMO-1B")
prompt = "Imagine you're helping someone who is feeling overwhelmed. How do you feel in this situation?"
messages = [
{"role": "system", "content": "You are a helpful and emotional assistant that will always respond in EMO style"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)