metadata

license: llama2
datasets:
  - cerebras/SlimPajama-627B
language:
  - en
pipeline_tag: text-generation
tags:
  - Deci AI
  - DeciLM
model-index:
  - name: DeciLM 6B
    results:
      - task:
          type: text-generation
        dataset:
          type: ai2/arc
          name: ai2_arc
        metrics:
          - name: ARC Challenge
            type: ARC Challenge
            value: 42.06
            verified: false
      - task:
          type: text-generation
        dataset:
          type: ai2/arc
          name: ai2_arc
        metrics:
          - name: ARC Easy
            type: ARC Easy
            value: 70.02
            verified: false
      - task:
          type: text-generation
        dataset:
          type: boolq
          name: boolq
        metrics:
          - name: BoolQ
            type: BoolQ
            value: 71.01
            verified: false
      - task:
          type: text-generation
        dataset:
          type: hellaswag
          name: hellaswag
        metrics:
          - name: HellaSwag
            type: HellaSwag
            value: 74.58
            verified: false
      - task:
          type: text-generation
        dataset:
          type: LAMBDA
          name: OpenAI LAMBDA
        metrics:
          - name: LAMBDA
            type: LAMBDA
            value: 69.78
            verified: false
      - task:
          type: text-generation
        dataset:
          type: OpenBookQA
          name: openbookqa
        metrics:
          - name: OpenBookQA
            type: OpenBookQA
            value: 34
            verified: false
      - task:
          type: text-generation
        dataset:
          type: PIQA
          name: piqa
        metrics:
          - name: PIQA
            type: PIQA
            value: 77.09
            verified: false
      - task:
          type: text-generation
        dataset:
          type: truthful_qa
          name: truthful_qa
        metrics:
          - name: TruthfulQA
            type: TruthfulQA
            value: 36.19
            verified: false
      - task:
          type: text-generation
        dataset:
          type: winogrande
          name: winogrande
        metrics:
          - name: Winogrande
            type: Winogrande
            value: 68.03
            verified: false

DeciLM 6B

DeciLM 6B is a 5.7 billion parameter decoder-only text generation model. With a context window of 4096 tokens, the highly efficient model uses variable Grouped-Query Attention (GQA) to achieve an optimal balance between performance and computational efficiency. The model's architecture was generated using Deci's proprietary Neural Architecture Search-based technology, AutoNAC.

Model Details

Model Description

Deci developed and publically released the DeciLM 6B large language model, a pretrained, high-efficiency generative text model with 5.7 billion parameters. DeciLM 6B outpaces pretrained models in its class, with a throughput that's up to 15 times that of Llama 2 7B's. DeciLM-6B was further LoRA fine-tuned for instruction following on a subset of the OpenOrca dataset, creating DeciLM 6B Instruct

Developed by: Deci
Model type: DeciLM is an auto-regressive language model using an optimized transformer decoder architecture that includes variable Grouped-Query Attention.
Language(s) (NLP): English
License: Llama 2 Community License Agreement

Model Architecture

Parameters	Layers	Heads	Sequence Length	GQA num_key_value_heads*	Hidden Size
5.7B	32	32	4096	Variable	4096

*AutoNAC was employed to optimize the selection of the GQA num_key_value_heads for each layer of the model.

Decoder layer: Varible Grouped Query Attention. Grouped Query Attention (GQA) was introduced in Ainslie et al., 2023
Position Embeddings: Dynamic NTK Scaling Rotary Position Embeddings Su et al., 2021

Model Sources

Uses

The model is intended for commercial and research use in English and can be fine-tuned for use in other languages.

How to Get Started with the Model

Use the code below to get started with the model.

# pip install -q transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

checkpoint = "Deci/DeciLM-6b"
device = "cuda" # for GPU usage or "cpu" for CPU usage

tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device)

inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
print(tokenizer.decode(outputs[0]))

Training Details

DeciLM 6B underwent training utilizing a subset of the SlimPajamas dataset, leveraging advanced proprietary methodologies allowing for fast training.

Evaluation

Below are DeciLM's 6B evaluation results.

Average	ARC Challenge*	ARC Easy*	BoolQ	HellaSwag*	LAMBDA OpenAI	OpenBookQA	PIQA	TruthfulQA	Winogrande
60.33	42.06	70.02	71.01	74.58	69.78	34	77.09	36.19	68.03
Accuracy-norm score*

Runtime Benchmarks

Inference Tool/Hardware	A10 (tokens/sec)
HF Inference Endpoints	652.49
Infery LLM	2,029.6

Throughput (tokens/sec) - Measured with optimal batch - HF Inference Endpoints BS 64, Infery LLM BS 128

How to Cite

Please cite this model using this format.

@misc{DeciFoundationModels,
title = {DeciLM 6B},
author = {DeciAI Research Team},
year = {2023}
url={[https://huggingface.co/Deci/DeciLM-6b](https://huggingface.co/Deci/DeciLM-6b)},
}