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))
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