amezasor's picture
update after review
b848cbd verified
|
raw
history blame
9.79 kB
metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - language
  - granite-3.0
model-index:
  - name: granite-3.0-8b-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-3.0-8B-Instruct

Model Summary: Granite-3.0-8B-Instruct is a 8B parameter model finetuned from Granite-3.0-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fintune Granite 3.0 models for languages beyond these 12 languages.

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 tasks
  • Function-calling tasks
  • Multilingual dialog use cases

Generation: This is a simple example of how to use Granite-3.0-8B-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-3.0-8b-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-3.0-8B-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: Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities, and (3) very small amounts of human-curated data. Please refer to Granite 3.0 Language Models technical report for more details on the individual categories and datasets.

Infrastructure: We train Granite 3.0 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 3.0 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks.