metadata
license: apache-2.0
datasets:
- oztrkoguz/Short-Story
language:
- en
metrics:
- accuracy
pipeline_tag: text2text-generation
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)