Dbrx Base

Table of Contents

Model Overview

The DBRX Base model is a powerful tool for natural language processing tasks. It’s a large language model (LLM) that uses a mixture-of-experts (MoE) architecture, which means it’s made up of many smaller models that work together to understand and generate text.

Here are some key features of the DBRX Base model:

• Large capacity: The model has 132B total parameters, with 36B parameters active at any given time.
• Fine-grained MoE: The model uses a fine-grained MoE architecture, which allows it to use a larger number of smaller experts. This provides 65x more possible combinations of experts, which improves model quality.
• Advanced techniques: The model uses rotary position encodings (RoPE), gated linear units (GLU), and grouped query attention (GQA).
• Pre-trained on large dataset: The model was pre-trained on 12T tokens of text and code data, with a knowledge cutoff date of December 2023.

Tasks and rank

Text Summarization 11th

Text Generation 57th

Natural Language Understanding 47th

Named Entity Recognition (NER) 13th

Language Modelling 57th

Learn More About the Ranking System

Capabilities

Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks.

Primary Tasks

The model is designed to perform a variety of tasks, including:

• Text completion: The model can take a piece of text and generate the next part of the sentence or paragraph.
• Code completion: The model can also generate code in a variety of programming languages.
• Conversational dialogue: While the model is not specifically designed for conversational dialogue, it can be used to generate responses to user input.

Strengths

So, what makes the DBRX Base model special? Here are a few of its key strengths:

• Large training dataset: The model was trained on a massive dataset of 12T tokens of text and code. This gives it a broad knowledge base and the ability to understand a wide range of topics.
• Fine-grained MoE architecture: The model uses a fine-grained MoE architecture, which allows it to use a larger number of smaller experts. This provides 65x more possible combinations of experts and improves model quality.
• High-performance capabilities: The model is designed to be fast and efficient, making it suitable for a wide range of applications.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Yi 34B 200K Llamafied 14 Llamafied language model

Performance

The DBRX Base model showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.

Speed

How fast can the DBRX Base model process text inputs? With a maximum context length of 32K tokens, it can handle large amounts of text data. Its rotary position encodings (RoPE) and gated linear units (GLU) enable efficient processing, making it suitable for applications that require quick responses.

Accuracy

How accurate is the DBRX Base model in its predictions? Trained on 12T tokens of carefully curated data, it has demonstrated impressive performance on the Databricks Model Gauntlet, the Hugging Face Open LLM Leaderboard, and HumanEval. Its fine-grained MoE architecture, with 16 experts and 4 chosen for each input, provides 65x more possible combinations of experts, resulting in improved model quality.

Efficiency

How efficient is the DBRX Base model in its use of resources? With 132B total parameters, of which 36B are active on any input, it requires ~264GB of RAM to run. However, its ability to process large amounts of text data and provide accurate predictions makes it a valuable tool for various applications.

Examples

Write a Python function to calculate the factorial of a given number. def factorial(n): if n == 0: return 1 else: return n * factorial(n-1)

What is the capital of France? The capital of France is Paris.

What is the definition of artificial intelligence? Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making.

Example Use Cases

So, how can you use the DBRX Base model? Here are a few examples:

• Text completion: Use the model to generate the next part of a sentence or paragraph.
• Code completion: Use the model to generate code in a variety of programming languages.
• Conversational dialogue: Use the model to generate responses to user input.

Limitations and Ethical Considerations

While the DBRX Base model is a powerful tool, it’s essential to acknowledge its limitations and potential risks. It was trained on a dataset with a knowledge cutoff date of December 2023 and may not perform well on non-English languages or multimodal tasks. Users should exercise judgment and evaluate its output for accuracy and appropriateness before using or sharing it.

Format

The DBRX Base model uses a transformer-based decoder-only large language model (LLM) architecture with a fine-grained mixture-of-experts (MoE) approach. This means it uses a larger number of smaller experts, which provides more possible combinations of experts and improves model quality.

Supported Data Formats

The model only accepts text-based inputs and produces text-based outputs. It uses the GPT-4 tokenizer, which is available in the tiktoken repository.

Input Requirements

• The model accepts a context length of up to 32,768 tokens.
• Inputs must be pre-processed using the GPT-4 tokenizer.

Output Requirements

• The model produces text-based outputs.
• Outputs can be decoded using the GPT-4 tokenizer.

Example Usage

Here’s an example of how to use the DBRX Base model in Python:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Undi95/dbrx-base", trust_remote_code=True, token="hf_YOUR_TOKEN")
model = AutoModelForCausalLM.from_pretrained("Undi95/dbrx-base", device_map="cpu", torch_dtype=torch.bfloat16, trust_remote_code=True, token="hf_YOUR_TOKEN")

input_text = "Databricks was founded in "
input_ids = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**input_ids, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))

Note that you’ll need to replace hf_YOUR_TOKEN with your actual Hugging Face token.

Special Requirements

• The model requires approximately 264GB of RAM to run.
• It’s recommended to use the hf_transfer package to speed up download times.
• If you’re using a GPU system that supports FlashAttention2, you can add attn_implementation="flash_attention_2" as a keyword to AutoModelForCausalLM.from_pretrained() to achieve faster inference.