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---
base_model: unsloth/mistral-7b-bnb-4bit
library_name: peft
license: mit
datasets:
- yahma/alpaca-cleaned
language:
- en
pipeline_tag: text-generation
tags:
- physics
- conversational
---
How to use :
```python
!pip install peft accelerate bitsandbytes
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the configuration for the fine-tuned model
model_id = "Vijayendra/QST-Mistral-7b"
config = PeftConfig.from_pretrained(model_id)
# Load the base model and the fine-tuned model
base_model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path)
model = PeftModel.from_pretrained(base_model, model_id)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
# Prepare the input for inference
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:
{}
### Input:
{}
### Response:
{}"""
instruction = "Explain the significance of cyclic operators in machine learning theory."
input_text = "Provide a detailed explanation suitable for a beginner in quantum machine learning."
formatted_prompt = prompt.format(instruction, input_text, "")
# Tokenize the input
inputs = tokenizer(
formatted_prompt,
return_tensors="pt",
max_length=2048,
truncation=True
).to("cuda")
# Run inference
model.to("cuda")
outputs = model.generate(
**inputs,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_k=50
)
# Decode and print the output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response) |