How to use from the
Use from the
Transformers library
# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Danielbrdz/Barcenas-Tiny-1.1b-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Danielbrdz/Barcenas-Tiny-1.1b-DPO")
model = AutoModelForCausalLM.from_pretrained("Danielbrdz/Barcenas-Tiny-1.1b-DPO")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
Quick Links

Barcenas Tiny 1.1b DPO

It is a model based on the famous TinyLlama/TinyLlama-1.1B-Chat-v1.0 and trained with DPO using the Intel/orca_dpo_pairs dataset.

With its reinforcement based training we hope to improve the Tiny model in a huge way and have a better model with better responses with a small size and accessible to most people.

Many thanks to Maxime Labonne (mlabonne) for his tutorial on how to train a LLM model using DPO, without his tutorial this model would not have been possible.

Made with ❤️ in Guadalupe, Nuevo Leon, Mexico 🇲🇽

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