Llama-3-8B-Instruct-80K-QLoRA

<div align="center"> <h1>Llama-3-8B-Instruct-80K-QLoRA</h1>

<a href="https://github.com/FlagOpen/FlagEmbedding/tree/master/Long_LLM/longllm_qlora">[Data&Code]</a>

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We extend the context length of Llama-3-8B-Instruct to 80K using QLoRA and 3.5K long-context training data synthesized from GPT-4. The entire training cycle is super efficient, which takes 8 hours on a 8xA800 (80G) machine. Yet, the resulted model achieves remarkable performance on a series of downstream long-context evaluation benchmarks.

Evaluation

All the following evaluation results can be reproduced following instructions here.

Needle in a Haystack

We evaluate the model on the Needle-In-A-HayStack task using the official setting. The blue vertical line indicates the training context length, i.e. 80K.

<img src="data/needle.png"></img>

LongBench

We evaluate the model on LongBench using 32K context length and the official prompt template. For meta-llama/Meta-Llama-3-8B-Instruct, we use 8K context length.

InfiniteBench

We evaluate the model on InfiniteBench using 80K context length and the official prompt template. The results of GPT-4 is copied from the paper. For meta-llama/Meta-Llama-3-8B-Instruct, we use 8K context length.

Topic Retrieval

We evaluate the model on Topic Retrieval task with [5,10,15,20,25,30,40,50,60,70] topics.

<img src="data/topic.png"></img>

MMLU

We evaluate the model's zero-shot performance on MMLU benchmark as a reflection of its short-context capability.

Environment

torch==2.2.2
flash_attn==2.5.6
transformers==4.39.3
peft==0.10.0

Usage

import json
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
peft_id = "namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA"

torch_dtype = torch.bfloat16
# place the model on GPU
device_map = {"": "cuda"}

tokenizer = AutoTokenizer.from_pretrained(model_id)

base_model = AutoModelForCausalLM.from_pretrained(
  model_id, 
  torch_dtype=torch.bfloat16,
  device_map=device_map,
  attn_implementation="flash_attention_2",

  # NOTE: expand rope base
  rope_theta=200e6,
)

model = PeftModel.from_pretrained(
    base_model, 
    peft_id,
    torch_dtype=torch.bfloat16,
    device_map=device_map,
)
# NOTE: merge LoRA weights
model = model.merge_and_unload().eval()

with torch.no_grad():
  # short context
  messages = [{"role": "user", "content": "Tell me about yourself."}]
  inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
  outputs = model.generate(**inputs, max_new_tokens=50)[:, inputs["input_ids"].shape[1]:]
  print(f"Input Length: {inputs['input_ids'].shape[1]}")
  print(f"Output:       {tokenizer.decode(outputs[0])}")

  # long context
  with open("data/narrativeqa.json", encoding="utf-8") as f:
    example = json.load(f)
  messages = [{"role": "user", "content": example["context"]}]
  inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True).to("cuda")
  outputs = model.generate(**inputs, do_sample=False, top_p=1, temperature=1, max_new_tokens=20)[:, inputs["input_ids"].shape[1]:]
  print("*"*20)
  print(f"Input Length: {inputs['input_ids'].shape[1]}")
  print(f"Answers:      {example['answer']}")
  print(f"Prediction:   {tokenizer.decode(outputs[0])}")

You may observe messages like: This is a friendly reminder - the current text generation call will exceed the model's predefined maximum length (8192). Depending on the model, you may observe exceptions, performance degradation, or nothing at all. or Setting pad_token_id to eos_token_id:128001 for open-end generation. They do not matter. Just ignore them.