metadata

pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - language
  - granite-3.0
model-index:
  - name: granite-3.0-2b-instruct
    results:
      - task:
          type: text-generation
        dataset:
          type: human-exams
          name: MMLU
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: human-exams
          name: MMLU-Pro
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: human-exams
          name: AGI-Eval
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: WinoGrande
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: OBQA
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: SIQA
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: PIQA
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: Hellaswag
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: commonsense
          name: TruthfulQA
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: reading-comprehension
          name: BoolQ
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: reading-comprehension
          name: SQuAD v2
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: reasoning
          name: ARC-C
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: reasoning
          name: GPQA
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: reasoning
          name: BBH
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: code
          name: HumanEval
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: code
          name: MBPP
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: math
          name: GSM8K
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: math
          name: MATH
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false
      - task:
          type: text-generation
        dataset:
          type: multilingual
          name: MGSM
        metrics:
          - name: pass@1
            type: pass@1
            value: null
            veriefied: false

Granite-3.0-2B-Instruct

Model Summary

Granite-3.0-2B-Instruct is a lightweight and open-source 2B parameter model fine tuned from Granite-3.0-2B-Base on a combination of open-source and proprietary instruction data with a permissively licensed. This language model is designed to excel in instruction following tasks such as summarization, problem-solving, text translation, reasoning, code tasks, funcion-calling, and more.

Developers: IBM Research
GitHub Repository: ibm-granite/granite-language-models
Website: Granite Docs
Paper: Granite Language Models
Release Date: October 21st, 2024
License: Apache 2.0.

Supported Languages

English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (Simplified)

Usage

Intended use

The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including bussiness applications.

Capabilities

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related
Function-calling
Multilingual dialog use cases

Generation

This is a simple example of how to use Granite-3.0-2B-Instruct model.

Install the following libraries:

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Then, copy the snippet from the section that is relevant for your usecase.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "auto"
model_path = "ibm-granite/granite-3.0-2b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
    { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(chat, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens, 
                        max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output)

Model Architeture

Granite-3.0-2B-Instruct is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA and RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embbeddings.

Model	2B Dense	8B Dense	1B MoE	3B MoE
Embedding size	2048	4096	1024	1536
Number of layers	40	40	24	32
Attention head size	64	128	64	64
Number of attention heads	32	32	16	24
Number of KV heads	8	8	8	8
MLP hidden size	8192	12800	512	512
MLP activation	SwiGLU	SwiGLU	SwiGLU	SwiGLU
Number of Experts	—	—	32	40
MoE TopK	—	—	8	8
Initialization std	0.1	0.1	0.1	0.1
Sequence Length	4096	4096	4096	4096
Position Embedding	RoPE	RoPE	RoPE	RoPE
# Paremeters	2.5B	8.1B	1.3B	3.3B
# Active Parameters	2.5B	8.1B	400M	800M
# Training tokens	12T	12T	10T	10T

Training Data

Granite Language Instruct models are trained on a collection of publicly available datasets with non-restrictive license, as well as an IBM collection of synthetic datasets. We annotated and filtered these datasets to only include high-quality instances from each of them in our final mixture. This dataset selection is representative of the following domains:

English datasets: Open-Platypus, WebInstructSub, OASST-OctoPack, Daring-Anteater, SoftAge-Multiturn, Glaive-RAG-v1 , EvolKit-20k, Magpie-Phi3-Pro-300K-Filtered.
Multilingual datasets: Aya Dataset and IBM Synthetic datasets (e.g., Blue Multilingual, Daring Anteater Translated).
Code datasets: Glaive Code Assistant V3, SQL Create Context Instruction, and Self-OSS-Instruct-SC2. Single and multi-turn IBM synthetic datasets, including a set of datasets generated via the evol-instruct method.
Math: MetaMathQA, StackMathQA, and MathInstruct
Tools: xlam-function-calling, Glaive Function Calling V2, Hermes Function Calling V1, and IBM Synthetic API data.
Safety: SimpleSafetyTests, HarmBench Behaviors, Strong Reject, AdvBench, MistralGuard, Do-Not-Answer, and IBM Synthetic data for safety.

Infrastructure

We train the Granite Language models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.

Ethical Considerations and Limitations

Granite instruct models are primarily finetuned using instruction-response pairs mostly in English, but also in German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese (Simplified). As this model has been exposed to multilingual data, it can handle multilingual dialog use cases with a limited performance in non-English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. The model also inherits ethical considerations and limitations from its base model. For more information, please refer to Granite-3.0-2B-Base model card.