ZuluVision/MoviiGen1.1_Prompt_Rewriter

Model

10

stars

3

commits

3

repos using this model

3

linked in READMEs

May 17, 2025

updated

art
qwen2
safetensors
Browse cluster: Text-to-Image Generation & ControlNet

README

MoviiGen1.1 Prompt Rewriter

This is a fine-tuned version of Qwen2.5-7B-Instruct by MoviiGen1.1 corresponding dataset.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZuluVision/MoviiGen1.1_Prompt_Rewriter"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "A beautiful girl is playing with a dog in a park."
messages = [
    {"role": "system", "content": "You are an advanced AI model tasked with generating and extending structured and detailed video captions. You must respond in the language used by the user."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
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]

Contributors

xilanhua12138

3 commits

ZuluVision/MoviiGen1.1_Prompt_Rewriter

Model

10

stars

3

commits

3

repos using this model

3

linked in READMEs

May 17, 2025

updated

art
qwen2
safetensors
Browse cluster: Text-to-Image Generation & ControlNet

README

MoviiGen1.1 Prompt Rewriter

This is a fine-tuned version of Qwen2.5-7B-Instruct by MoviiGen1.1 corresponding dataset.

Quick Start

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZuluVision/MoviiGen1.1_Prompt_Rewriter"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "A beautiful girl is playing with a dog in a park."
messages = [
    {"role": "system", "content": "You are an advanced AI model tasked with generating and extending structured and detailed video captions. You must respond in the language used by the user."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=1024
)
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]

Contributors

xilanhua12138

3 commits