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---
pipeline_tag: text-generation
inference: false
license: apache-2.0
# datasets:
# metrics:
# - code_eval
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:
veriefied: false
- task:
type: text-generation
dataset:
type: human-exams
name: MMLU-Pro
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: human-exams
name: AGI-Eval
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: WinoGrande
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: OBQA
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: SIQA
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: PIQA
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: Hellaswag
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: commonsense
name: TruthfulQA
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: reading-comprehension
name: BoolQ
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: reading-comprehension
name: SQuAD v2
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: ARC-C
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: GPQA
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: reasoning
name: BBH
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: code
name: MBPP
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: math
name: GSM8K
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: math
name: MATH
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
- task:
type: text-generation
dataset:
type: multilingual
name: MGSM
metrics:
- name: pass@1
type: pass@1
value:
veriefied: false
---
<!--  -->
# Granite-3.0-8B-Instruct
## Model Summary
**Granite-3.0-8B-Instruct** is a lightweight and open-source 8B parameter model fine tuned from *Granite-3.0-8B-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.
<!-- The lightweight and open-source nature of this model makes it an excellent choice to serve as backbone of real-time applications such as chatbots and conversational agents. -->
- **Developers:** IBM Research
- **GitHub Repository:** [ibm-granite/granite-language-models](https://github.com/ibm-granite/granite-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Granite Language Models](https://) <!-- TO DO: Update github repo link when it is ready -->
- **Release Date**: October 21st, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-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-3.0-8B-Instruct** model.
Install the following libraries:
```shell
pip install torch torchvision torchaudio
pip install accelerate
pip install transformers
```
Then, copy the snippet from the section that is relevant for your usecase.
```python
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)
```
<!-- TO DO: function-calling-example
-->
<!-- ['<|start_of_role|>user<|end_of_role|>Please list one IBM Research laboratory located in the United States. You should only output its name and location.<|end_of_text|>\n<|start_of_role|>assistant<|end_of_role|>1. IBM Research - Almaden, San Jose, California<|end_of_text|>'] -->
## 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 |
<!-- TO DO: To be completed once the paper is ready, we may changed title to Supervised Finetuning -->
## 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](https://huggingface.co/datasets/garage-bAInd/Open-Platypus), [WebInstructSub](https://huggingface.co/datasets/TIGER-Lab/WebInstructSub), [OASST-OctoPack](https://huggingface.co/datasets/bigcode/oasst-octopack), [Daring-Anteater](https://huggingface.co/datasets/nvidia/Daring-Anteater), [SoftAge-Multiturn](https://huggingface.co/datasets/SoftAge-AI/multi-turn_dataset), [Glaive-RAG-v1 ](https://huggingface.co/datasets/glaiveai/RAG-v1 ), [EvolKit-20k](https://huggingface.co/datasets/arcee-ai/EvolKit-20k ), [Magpie-Phi3-Pro-300K-Filtered](https://huggingface.co/datasets/Magpie-Align/Magpie-Phi3-Pro-300K-Filtered).
* Multilingual datasets: [Aya Dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) and IBM Synthetic datasets (e.g., Blue Multilingual, Daring Anteater Translated).
* Code datasets: [Glaive Code Assistant V3](https://huggingface.co/datasets/glaiveai/glaive-code-assistant-v3), [SQL Create Context Instruction](https://huggingface.co/datasets/bugdaryan/sql-create-context-instruction), and [Self-OSS-Instruct-SC2](https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k). Single and multi-turn IBM synthetic datasets, including a set of datasets generated via the evol-instruct method.
* Math: [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA), [StackMathQA](https://huggingface.co/datasets/math-ai/StackMathQA ), and [MathInstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
* Tools: [xlam-function-calling](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k), [Glaive Function Calling V2](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2), [Hermes Function Calling V1](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1), and IBM Synthetic API data.
* Safety: [SimpleSafetyTests](https://huggingface.co/datasets/Bertievidgen/SimpleSafetyTests), [HarmBench Behaviors](https://github.com/centerforaisafety/HarmBench/blob/main/data/behavior_datasets/harmbench_behaviors_text_all.csv), [Strong Reject](https://github.com/alexandrasouly/strongreject/blob/main/strongreject_dataset/strongreject_dataset.csv), [AdvBench](https://huggingface.co/datasets/walledai/AdvBench), [MistralGuard](https://huggingface.co/datasets/natolambert/xstest-v2-copy), [Do-Not-Answer](https://huggingface.co/datasets/LibrAI/do-not-answer), and IBM Synthetic data for safety.
<!-- CHECK: removed Vela, only talk about blue-vela-->
## 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.
<!-- TO DO: Check multilingual statement once the paper is ready -->
## 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-3.0-8B-Base](https://huggingface.co/ibm-granite/granite-3.0-8b-base)* model card.
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