metadata

tags:
  - llama
  - adapter-transformers
  - llama-2
datasets:
  - timdettmers/openassistant-guanaco
license: apache-2.0
pipeline_tag: text-generation

OpenAssistant QLoRA Adapter for Llama-2 7B

QLoRA adapter for the Llama-2 7B (meta-llama/Llama-2-7b-hf) model trained for instruction tuning on the timdettmers/openassistant-guanaco dataset.

This adapter was created for usage with the Adapters library.

Usage

First, install adapters:

pip install -U adapters

Now, the model and adapter can be loaded and activated like this:

import adapters
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig

model_id = "meta-llama/Llama-2-7b-hf"
adapter_id = "AdapterHub/llama2-7b-qlora-openassistant"

model = AutoModelForCausalLM.from_pretrained(
    model_id,    
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
        load_in_4bit=True,
        bnb_4bit_quant_type="nf4",
        bnb_4bit_use_double_quant=True,
        bnb_4bit_compute_dtype=torch.bfloat16,
    ),
    torch_dtype=torch.bfloat16,
)
adapters.init(model)

adapter_name = model.load_adapter(adapter_id, source="hf", set_active=True)

tokenizer = AutoTokenizer.from_pretrained(model_id)

Inference

Inference can be done via standard methods built in to the Transformers library. We add some helper code to properly prompt the model first:

from transformers import StoppingCriteria

# stop if model starts to generate "### Human:"
class EosListStoppingCriteria(StoppingCriteria):
    def __init__(self, eos_sequence = [12968, 29901]):
        self.eos_sequence = eos_sequence

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        last_ids = input_ids[:,-len(self.eos_sequence):].tolist()
        return self.eos_sequence in last_ids

def prompt_model(model, text: str):
    batch = tokenizer(f"### Human: {text} ### Assistant:", return_tensors="pt")
    batch = batch.to(model.device)
    
    with torch.cuda.amp.autocast():
        output_tokens = model.generate(**batch, stopping_criteria=[EosListStoppingCriteria()])

    # skip prompt when decoding
    decoded = tokenizer.decode(output_tokens[0, batch["input_ids"].shape[1]:], skip_special_tokens=True)
    return decoded[:-10] if decoded.endswith("### Human:") else decoded

Now, to prompt the model:

prompt_model(model, "Please explain NLP in simple terms.")

Weight merging

To decrease inference latency, the LoRA weights can be merged with the base model:

model.merge_adapter(adapter_name)

Architecture & Training

Training was run with the code in this notebook.

The LoRA architecture closely follows the configuration described in the QLoRA paper:

r=64, alpha=16
LoRA modules added in output, intermediate and all (Q, K, V) self-attention linear layers

The adapter is trained similar to the Guanaco models proposed in the paper:

Dataset: timdettmers/openassistant-guanaco
Quantization: 4-bit QLoRA
Batch size: 16, LR: 2e-4, max steps: 1875
Sequence length: 512