Granite-3.0-8B-Instruct
Model Summary
- Developer: IBM Research
- GitHub Repository: ibm-granite/granite-language-models
- Website: Granite Docs
- Paper: Granite Language Models
- Release Date: October 21st, 2024
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.
Third-Party Community Consideration
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party’s requirements for this application and use case; see link to Non-NVIDIA Granite-3.0-8B-Base model card.
License/Terms of Use:
GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service; and the use of this model is governed by the NVIDIA AI Foundation Models Community License Agreement. ADDITIONAL INFORMATION: Apache 2.0 License.
Model Architecture:
Architecture Type: [Transformer]
Network Architecture: [Other - Dense]
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 |
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
Input:
Input Type(s): Text
Input Format(s): String
Input Parameters: min_tokens, max_tokens, temperature, top_p, stop, frequency_penalty, presence_penalty
Other Properties Related to Input: Supported Languages include
English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (Simplified)
Output:
Output Type(s): Text
Output Format: String
Output Parameters: None
Other Properties Related to Output: [None]
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)
Training Data
Granite Language Instruct models are trained on a selection of open-srouce instruction datasets with a non-restrictive license, as well as a collection of synthetic datasets created by IBM. Together, these instruction datasets are a solid representation of the following domains: English, multilingual, code, math, tools, and 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.
Model Version(s):
Granite-Dense-3.0-instruct
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.
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their internal model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.
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