salma-remyx/test_startup_advice
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How to use salma-remyx/test_train_general_1 with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2b-it")
model = PeftModel.from_pretrained(base_model, "salma-remyx/test_train_general_1")test_train_general_1 uses google/gemma-2b-it as the backbone.
This model is fine-tuned on the salma-remyx/test_startup_advice_50_samples dataset designed to enhance specific reasoning capabilities.
This model was fine-tuned for task 'llm' using data generated on 20:37 January 08, 2025.
Use the code snippet below to load the base model and apply the adapter for inference:
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load the base model
base_model_name = "google/gemma-2b-it"
adapter_path = "/path/to/adapter" # Replace with actual adapter path
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
# Apply the adapter
model = PeftModel.from_pretrained(base_model, adapter_path)
model = model.merge_and_unload()
# Run inference
input_text = "Your input text here"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Base model
google/gemma-2b-it