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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, 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
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the base model and tokenizer
tokenizer_model = "unsloth/Phi-3-mini-4k-instruct"
lora_model = "oztrkoguz/phi3_short_story_merged_bfloat16"
tokenizer = AutoTokenizer.from_pretrained(tokenizer_model)
model = AutoModelForCausalLM.from_pretrained(lora_model).to("cuda")
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
create a short story from this keywords
### Input:
{}
### Response:
{}"""
# Use the merged model for inference
inputs = tokenizer(
[
alpaca_prompt.format(
"cat, dog, human",
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
with torch.no_grad():
output = model.generate(
**inputs,
max_length=100
)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
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