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# alpaca-lora
This repository contains code for reproducing the Stanford Alpaca results. Users will need to have LLaMA weights on hand and be ready to fork `transformers`.
1. Install dependencies
```
pip install -q bitsandbytes datasets accelerate loralib
pip install -q git+https://github.com/zphang/transformers@llama_push
pip install -q git+https://github.com/huggingface/peft.git\
```
2. Convert weights
```
python conversion.py --input_dir [LLAMA_DIR]/LLaMA --model_size 7B --output_dir ./7B
```
3. Modify hyperparams in `finetune.py`
```
MICRO_BATCH_SIZE = 12
BATCH_SIZE = 36
EPOCHS = 3
LEARNING_RATE = 2e-5
```
4. Run experiments
```
python finetune.py
```

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@ -86,7 +86,6 @@ data = data.map(
)
)
DATA_SIZE = 51368
MICRO_BATCH_SIZE = 12
BATCH_SIZE = 36
GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE

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import torch
from peft import get_peft_model, PeftConfig, LoraConfig, PeftModel
from transformers import LLaMATokenizer, LLaMAForCausalLM
tokenizer = LLaMATokenizer.from_pretrained("./7B/tokenizer")
model = LLaMAForCausalLM.from_pretrained(
"./7B/llama-7b",
load_in_8bit=True,
device_map="auto",
)
model = PeftModel.from_pretrained(model, "./outputs")
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:
Sort the following numbers.
### Input:
5, 2, 3
### Response:"""
inputs = tokenizer(
PROMPT,
return_tensors="pt",
)
generation_output = model.generate(
**inputs, return_dict_in_generate=True, output_scores=True, max_new_tokens=50
)
for s in generation_output.sequences:
print(tokenizer.decode(s))