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metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - language
  - granite-3.0
model-index:
  - name: granite-3.0-3b-a800m-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-3.0-3B-A800M-Instruct

Model Summary

Granite-3.0-3B-A800M-Instruct is a lightweight and open-source 3B parameter model fine tuned from Granite-3.0-3B-A800M-Base-4K 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.

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-3.0-3B-A800M-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-3.0-3b-a800m-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-3.0-3B-A800M-Instruct is based on a decoder-only sparse Mixture of Experts(MoE) transformer architecture. Core components of this architecture are: Fine-grained Experts, Dropless Token Routing, and Load Balancing Loss.

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

This model is trained on a mix of open-source and proprietary datasets.

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-3.0-3B-A800M-Base-4K model card.