tags, library_name, base_model, widget, license, datasets

tags	library_name	base_model	widget	license	datasets
autotrain text-generation-inference text-generation peft	transformers	Qwen/Qwen2.5-1.5B-Instruct	messages role content user What is your favorite condiment?	other	HumanLLMs/Human-Like-DPO-Dataset

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "yasserrmd/Human-Like-Qwen2.5-1.5B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)