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---
language:
- en
library_name: peft
tags:
- llama
- lora
- peft
license: apache-2.0
---
[Low-Rank-Adaption (LoRA)](https://paperswithcode.com/paper/lora-low-rank-adaptation-of-large-language) of [LLAMA 6B model](https://paperswithcode.com/paper/llama-open-and-efficient-foundation-language-1) that is fine-tuned with [Stanford Alpaca instruction dataset](https://github.com/tatsu-lab/stanford_alpaca) using [PEFT](https://github.com/huggingface/peft).
This model is trained based on the script provided in https://github.com/tloen/alpaca-lora.
> You might need to install the latest transformers from github for Llama support.
```python
from peft import PeftModel
from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig
tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LlamaForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model, "tloen/alpaca-lora-7b",
torch_dtype=torch.float16
)
def generate_prompt(instruction, input=None):
if input:
return f"""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:
{instruction}
### Input:
{input}
### Response:"""
else:
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response:"""
model.eval()
def evaluate(
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=4,
**kwargs,
):
prompt = generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=2048,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s)
return output.split("### Response:")[1].strip()
```
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