README.md

---
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()
```