metadata

license: mit
pipeline_tag: text-generation

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

[Data&Code]

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.

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.

Model	Single-Doc QA	Multi-Doc QA	Summarization	Few-Shot Learning
meta-llama/Meta-Llama-3-8B-Instruct	37.33	36.04	26.83	69.56
gradientai/Llama-3-8B-Instruct-262k	37.29	31.20	26.18	67.25
Llama-3-8B-Instruct-80K-QLoRA	43.57	43.07	28.93	69.15

InfiniteBench

We evaluate the model on InfiniteBench using 80K context length and the official prompt template. The results of GPT4 is copied from the paper. For meta-llama/Meta-Llama-3-8B-Instruct, we use 8K context length.

Model	LongBookQA Eng
GPT4	22.22
meta-llama/Meta-Llama-3-8B-Instruct	7.00
gradientai/Llama-3-8B-Instruct-262k	20.30
Llama-3-8B-Instruct-80K-QLoRA	30.92

Topic Retrieval

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

MMLU

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

Model	STEM	Social Sciences	Humanities	Others	Avg
meta-llama/Meta-Llama-3-8B-Instruct	0.5387	0.7566	0.6944	0.6975	0.6591
gradientai/Llama-3-8B-Instruct-262k	0.5210	0.7326	0.6715	0.6980	0.6434
Llama-3-8B-Instruct-80K-QLoRA	0.5310	0.7324	0.6732	0.6879	0.6444

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,
  max_position_embeddings=81920,
)

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])}")