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

[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.

NOTE: This repo contains the quantized model of namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged. The quantization is conducted with llama.cpp (Q4_K_M and Q8_0).

All the following evaluation results are based on the UNQUANTIZED MODEL. They can be reproduced following instructions here. However, after quantization, you may observe quality degradation.

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.

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 Synthetic Code Avg
meta-llama/Meta-Llama-3-8B-Instruct 37.33 36.04 26.83 69.56 37.75 53.24 43.20
gradientai/Llama-3-8B-Instruct-262k 37.29 31.20 26.18 67.25 44.25 62.71 43.73
Llama-3-8B-Instruct-80K-QLoRA-Merged 43.57 43.07 28.93 69.15 48.50 51.95 47.19

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.

Model LongBookQA Eng LongBookSum Eng
GPT-4 22.22 14.73
meta-llama/Meta-Llama-3-8B-Instruct 7.00 16.40
gradientai/Llama-3-8B-Instruct-262k 20.30 10.34
Llama-3-8B-Instruct-80K-QLoRA-Merged 30.92 14.73

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
Llama-2-7B-Chat 35.92 54.37 51.74 51.42 47.22
Mistral-7B-v0.2-Instruct 48.79 69.95 64.99 61.64 60.10
meta-llama/Meta-Llama-3-8B-Instruct 53.87 75.66 69.44 69.75 65.91
gradientai/Llama-3-8B-Instruct-262k 52.10 73.26 67.15 69.80 64.34
Llama-3-8B-Instruct-80K-QLoRA-Merged 53.10 73.24 67.32 68.79 64.44

Environment

llama_cpp
torch==2.1.2
transformers==4.39.3

Usage

huggingface-cli download namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF --local-dir . --local-dir-use-symlinks False

In python,

from llama_cpp import Llama

llm = Llama(
  model_path="./Llama-3-8B-Instruct-80K-QLoRA-Merged-Q4_K_M.gguf",  # path to GGUF file
  n_ctx=81920,
  n_threads=96,
  n_gpu_layers=32,
)

with open("./data/needle.txt") as f:
    text = f.read()
    inputs = f"{text}\n\nWhat is the best thing to do in San Francisco?"

print(
  llm.create_chat_completion(
    messages = [
        {
            "role": "user",
            "content": inputs
        }
    ],
    temperature=0,
    max_tokens=50
  )
)

# The best thing to do in San Francisco is sitting in Helmer Dolores Park on a sunny day, eating a double cheeseburger with ketchup, and watching kids playing around.
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GGUF
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llama

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Inference Examples
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