YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Model Card
Fine tuned EleutherAI/pythia-410m using gokaygokay/prompt_description_stable_diffusion_3k dataset.
Direct Use
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "gokaygokay/phytia410m_desctoprompt"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Your description
test_description = """
View to a rustic terrace filled with pots with autumn flowers and a vine full of red leaves and bunches of grapes.
in the foreground a wooden table with a copious breakfast, coffee, bowls, vases and plates with fruits, nuts, chestnuts, hazelnuts, breads and buns.
"""
prompt_template = """### Description:
{description}
### Prompt:
"""
text = prompt_template.format(description=test_description)
def inference(text, model, tokenizer, max_input_tokens=1000, max_output_tokens=200):
# Tokenize
input_ids = tokenizer.encode(
text,
return_tensors="pt",
truncation=True,
max_length=max_input_tokens
)
# Generate
device = model.device
generated_tokens_with_prompt = model.generate(
input_ids=input_ids.to(device),
max_length=max_output_tokens,
)
# Decode
generated_text_with_prompt = tokenizer.batch_decode(generated_tokens_with_prompt, skip_special_tokens=True)
# Strip the prompt
generated_text_answer = generated_text_with_prompt[0][len(text):]
return generated_text_answer
print("Description input (test):", text)
print("Finetuned model's prompt: ")
print(inference(text, model, tokenizer))
Citation and attribution
This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.
@software{aydogan2024phytia410m_desctoprompt,
author = {Aydoğan, Gökay},
title = {{phytia410m_desctoprompt}},
year = {2024},
publisher = {Hugging Face},
url = {https://huggingface.co/gokaygokay/phytia410m_desctoprompt},
note = {Model repository; cite the base model and upstream datasets as required.}
}
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