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Hymba is a family of small language models (SLMs) featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with SSMs to achieve the best of both worlds: enhanced efficiency and improved performance. In Hymba, attention heads provide high-resolution recall, while SSM heads enable efficient context summarization.
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⚡ A minimal nanoGPT-style implementation of Hymba is now available in this repository. Try it out and adapt designs for your own models!
Performance comparison of Hymba-1.5B-Base against sub-2B models in terms of average accuracy, cache size (MB) relative to sequence length, and throughput (tok/sec).
As shown in the above figure, we compare Hymba-1.5B against sub-2B models (LlaMA 3.2-1B, OpenELM-1B, Phi-1.5, SmolLM2-1.7B, danube2-1.8B, Qwen2.5-1.5B) in terms of average task accuracy, cache size (MB) relative to sequence length, and throughput (tok/sec). In this set of experiments, the tasks include MMLU, ARC-C, ARC-E, PIQA, Hellaswag, Winogrande, and SQuAD-C, and the throughput is measured on an NVIDIA A100 with a sequence length of 8k and a batch size of 128 using PyTorch. For models encountering out-of-memory (OOM) issues during throughput measurement, we halve the batch size until the OOM is resolved to measure the maximal achievable throughput without OOM.
Please see:
Since our model employs FlexAttention, which relies on PyTorch 2.5 and other related dependencies, we provide two ways to set up the environment:
- [Local Install] Install the related packages using our provided
setup.sh(support CUDA 12.1/12.4):
wget --header="Authorization: Bearer YOUR_HF_TOKEN" https://huggingface.co/nvidia/Hymba-1.5B-Instruct/resolve/main/setup.sh
bash setup.sh
- [Docker] We have prepared a docker image with all of Hymba's dependencies installed. You can download our docker image and start a container using the following commands:
docker pull ghcr.io/tilmto/hymba:v1
docker run --gpus all -v /home/$USER:/home/$USER -it ghcr.io/tilmto/hymba:v1 bash
After setting up the environment, you can use the following script to chat with our model in the command line.
python chat.py
LMFlow is a complete pipeline for fine-tuning large language models.
The following steps provide an example of how to fine-tune the Hymba-1.5B-Base models using LMFlow.
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Using Docker
docker pull ghcr.io/tilmto/hymba:v1 docker run --gpus all -v /home/$USER:/home/$USER -it ghcr.io/tilmto/hymba:v1 bash -
Install LMFlow
git clone https://github.com/OptimalScale/LMFlow.git cd LMFlow conda create -n lmflow python=3.9 -y conda activate lmflow conda install mpi4py pip install -e . -
Fine-tune the model using the following command.
cd LMFlow bash ./scripts/run_finetune_hymba.sh
With LMFlow, you can also fine-tune the model on your custom dataset. The only thing you need to do is transform your dataset into the LMFlow data format. In addition to full-finetuniing, you can also fine-tune hymba efficiently with DoRA, LoRA, LISA, Flash Attention, and other acceleration techniques. For more details, please refer to the LMFlow for Hymba documentation.
We use LM Evaluation Harness to evaluate the model. The evaluation commands are as follows:
cd lm-evaluation-harness
pip install -e .
lm_eval --model hf --model_args pretrained=nvidia/Hymba-1.5B-Base,dtype=bfloat16,trust_remote_code=True \
--tasks mmlu \
--num_fewshot 5 \
--batch_size 1 \
--output_path ./hymba_HF_base_lm-results \
--log_samples
lm_eval --model hf --model_args pretrained=nvidia/Hymba-1.5B-Base,dtype=bfloat16,trust_remote_code=True \
--tasks arc_easy,arc_challenge,piqa,winogrande,hellaswag \
--num_fewshot 0 \
--batch_size 1 \
--output_path ./hymba_HF_base_lm-results \
--log_samples
lm_eval --model hf --model_args pretrained=nvidia/Hymba-1.5B-Base,dtype=bfloat16,trust_remote_code=True \
--tasks squad_completion \
--num_fewshot 1 \
--batch_size 1 \
--output_path ./hymba_HF_base_lm-results \
--log_samples
lm_eval --model hf --model_args pretrained=nvidia/Hymba-1.5B-Instruct,dtype=bfloat16,trust_remote_code=True \
--tasks mmlu,gsm8k \
--num_fewshot 5 \
--batch_size 1 \
--gen_kwargs max_length=8192 \
--output_path ./hymba_HF_instruct_lm-results \
--log_samples \
--apply_chat_template --fewshot_as_multiturn --device cuda:0
lm_eval --model hf --model_args pretrained=nvidia/Hymba-1.5B-Instruct,dtype=bfloat16,trust_remote_code=True \
--tasks ifeval,gpqa \
--num_fewshot 0 \
--batch_size 1 \
--gen_kwargs max_length=8192 \
--output_path ./hymba_HF_instruct_lm-results \
--log_samples \
--apply_chat_template --fewshot_as_multiturn --device cuda:0Hymba models are released under the NVIDIA Open Model License Agreement.
[Caution] During generation, the batch size needs to be 1. Our current implementation does not fully support padding of Meta tokens + SWA; this is a work in progress. Training and pre-filling support any batch size.
For other questions, please refer to the Trouble Shooting page and feel free to submit an issue for help.
If you find our work helpful, please consider citing our paper:
@article{hymba2024,
title={Hymba: A Hybrid-head Architecture for Small Language Models},
author={Xin Dong and Yonggan Fu and Shizhe Diao and Wonmin Byeon and Zijia Chen and Ameya Sunil Mahabaleshwarkar and Shih-Yang Liu and Matthijs Van Keirsbilck and Min-Hung Chen and Yoshi Suhara and Yingyan Lin and Jan Kautz and Pavlo Molchanov},
year={2024},
eprint={2411.13676},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.13676},
}
