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self-long/
SelfLong-Llama3.1-8B-Instruct-1M

Name: SelfLong-Llama3.1-8B-Instruct-1M
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SelfLong-Llama3.1-8B-Instruct-1M

Wang, Liang, Nan Yang, Xingxing Zhang, Xiaolong Huang, and Furu Wei. "Bootstrap Your Own Context Length." arXiv preprint arXiv:2412.18860 (2024).

Overview

The SelfLong series of Large Language Models (LLMs) are designed to handle extremely long contexts, reaching up to 1 million tokens. These models, with parameter sizes of 1B, 3B, and 8B, are initialized from the Llama-3.2 and Llama-3.1 architectures.

Performance (RULER-1M)

The following table presents the results of the SelfLong models on the RULER-1M benchmark. The numbers represent the RULER score averaged over 13 tasks at different support lengths.

Model	Support Length	32k	64k	128k	256k	512k	1M
Llama-3.2-1B-Instruct	128k	64.7	43.1	0.0	-	-	-
Llama-3.2-3B-Instruct	128k	77.8	70.4	0.8	-	-	-
Llama-3.1-8B-Instruct	128k	89.8	85.4	78.5	-	-	-
gradientai/Llama-3-8B-Instruct-Gradient-1048k	1M	81.8	78.6	77.2	74.2	70.3	64.3
SelfLong-1B-1M	1M	61.3	56.6	54.7	46.7	40.7	31.1
SelfLong-3B-1M	1M	80.5	78.0	75.5	68.8	58.5	38.8
SelfLong-8B-1M	1M	89.5	84.0	82.0	79.7	78.2	69.6

Note:

Bold indicates the best performance.
Underline indicates the second-best performance.
- indicates that the model does not support the given context length.

Evaluation on RULER-1M Dataset

To evaluate the SelfLong models on the RULER-1M dataset, you can follow these steps:

Start vllm server:

bash

PROC_PER_NODE=$(nvidia-smi --list-gpus | wc -l)
# Reduce this number if you have limited GPU memory
MAX_MODEL_LEN=1048576
MODEL_NAME_OR_PATH="self-long/SelfLong-Llama3.1-8B-Instruct-1M"

echo "Starting VLLM server..."
vllm serve "${MODEL_NAME_OR_PATH}" \
   --dtype auto \
   --disable-log-stats --disable-log-requests --disable-custom-all-reduce \
   --enable_chunked_prefill --max_num_batched_tokens 8192 \
   --tensor-parallel-size "${PROC_PER_NODE}" \
   --max-model-len "${MAX_MODEL_LEN}" \
   --gpu_memory_utilization 0.9 \
   --api-key token-123 &

Get Completions

python

from openai import OpenAI
from datasets import load_dataset

client = OpenAI(
    base_url="http://localhost:8000/v1",  # Default vLLM server address
    api_key="token-123"
)

ds = load_dataset('self-long/RULER-llama3-1M', f'niah_single_1_4k', split='validation')
prompt = ds[0]['input']

completion = client.completions.create(
    model='self-long/SelfLong-Llama3.1-8B-Instruct-1M',
    prompt=prompt,
    max_tokens=100,
)

print(prompt)
print(completion.choices[0].text)

For evaluation, please refer to the evaluation script provided in the RULER repository: https://github.com/NVIDIA/RULER/blob/main/scripts/eval/evaluate.py.

Note that different vLLM and Torch versions may produce slightly different decoding results.

References

@article{wang2024bootstrap,
  title={Bootstrap Your Own Context Length},
  author={Wang, Liang and Yang, Nan and Zhang, Xingxing and Huang, Xiaolong and Wei, Furu},
  journal={arXiv preprint arXiv:2412.18860},
  year={2024}
}