Hymba

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Model Overview

Hymba-1.5B-Instruct is a 1.5B parameter model finetuned from Hymba-1.5B-Base using a combination of open source instruction datasets and internally collected synthetic datasets. This model is finetuned with supervised fine-tuning and direct preference optimization.

Hymba-1.5B-Instruct is capable of many complex and important tasks like math reasoning, function calling, and role playing.

This model is ready for commercial use.

Model Developer: NVIDIA

Model Dates: Hymba-1.5B-Instruct was trained between September 4, 2024 and November 10th, 2024.

License: This model is released under the NVIDIA Open Model License Agreement.

Model Architecture

⚡️ We've released a minimal implementation of Hymba on GitHub to help developers understand and implement its design principles in their own models. Check it out! barebones-hymba.

Hymba-1.5B-Instruct has a model embedding size of 1600, 25 attention heads, and an MLP intermediate dimension of 5504, with 32 layers in total, 16 SSM states, 3 full attention layers, the rest are sliding window attention. Unlike the standard Transformer, each attention layer in Hymba has a hybrid combination of standard attention heads and Mamba heads in parallel. Additionally, it uses Grouped-Query Attention (GQA) and Rotary Position Embeddings (RoPE).

Features of this architecture:

• Fuse attention heads and SSM heads within the same layer, offering parallel and complementary processing of the same inputs.

• Introduce meta tokens that are prepended to the input sequences and interact with all subsequent tokens, thus storing important information and alleviating the burden of "forced-to-attend" in attention.

• Integrate with cross-layer KV sharing and global-local attention to further boost memory and computation efficiency.

Performance Highlights

• Hymba-1.5B-Instruct outperforms popular small language models and achieves the highest average performance across all tasks.

Model Usage

Step 1: Environment Setup

Since Hymba-1.5B-Instruct employs FlexAttention, which relies on Pytorch2.5 and other related dependencies, we provide two ways to setup 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-Base/resolve/main/setup.sh
bash setup.sh

• [Docker] A docker image is provided 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

Step 2: Chat with Hymba-1.5B-Instruct

After setting up the environment, you can use the following script to chat with our Model

from transformers import AutoModelForCausalLM, AutoTokenizer, StopStringCriteria, StoppingCriteriaList
import torch

# Load the tokenizer and model
repo_name = "nvidia/Hymba-1.5B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(repo_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_name, trust_remote_code=True)
model = model.cuda().to(torch.bfloat16)

# Chat with Hymba
prompt = input()

messages = [
    {"role": "system", "content": "You are a helpful assistant."}
]
messages.append({"role": "user", "content": prompt})

# Apply chat template
tokenized_chat = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to('cuda')
stopping_criteria = StoppingCriteriaList([StopStringCriteria(tokenizer=tokenizer, stop_strings="")])
outputs = model.generate(
    tokenized_chat,
    max_new_tokens=256,
    do_sample=False,
    temperature=0.7,
    use_cache=True,
    stopping_criteria=stopping_criteria
)
input_length = tokenized_chat.shape[1]
response = tokenizer.decode(outputs[0][input_length:], skip_special_tokens=True)

print(f"Model response: {response}")

The prompt template used by Hymba-1.5B-Instruct is as follows, which has been integrated into the tokenizer and can be applied using tokenizer.apply_chat_template:

{system prompt}

{prompt}
{tool response}

Finetuning Hymba

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.

1. 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
   

2. 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 .
   

3. 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](https://github