README.md

---
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: 
      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
---

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# 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.

- **Developers:** IBM Research
- **GitHub Repository:** [ibm-granite/granite-3.0-language-models](https://github.com/ibm-granite/granite-3.0-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/granite-3-language-models.pdf)
- **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, 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:

```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)
```

**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](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/granite-3-language-models.pdf) 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.

<!-- ## Citation
```
@misc{granite-models,
  author = {author 1, author2, ...},
  title = {},
  journal = {},
  volume = {},
  year = {2024},
  url = {https://arxiv.org/abs/0000.00000},
}
``` -->