Granite-4.0-Tiny-Preview
Model Summary: Granite-4-Tiny-Preview is a 7B parameter fine-grained hybrid mixture-of-experts (MoE) instruct model fine-tuned from Granite-4.0-Tiny-Base-Preview using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised fine-tuning, and model alignment using reinforcement learning.
- Developers: Granite Team, IBM
- Website: Granite Docs
- Release Date: May 2nd, 2025
- License: Apache 2.0
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may fine-tune this Granite model for languages beyond these 12 languages.
Intended Use: This model is designed to handle general instruction-following tasks and can be integrated into AI assistants across various domains, including business applications.
Capabilities
- Thinking
- Summarization
- Text classification
- Text extraction
- Question-answering
- Retrieval Augmented Generation (RAG)
- Code related tasks
- Function-calling tasks
- Multilingual dialog use cases
- Long-context tasks including long document/meeting summarization, long document QA, etc.
Installation: You need to install transformer from source to use this checkpoint.
HuggingFace PR: https://github.com/huggingface/transformers/pull/37658
Install transformer from source: https://huggingface.co/docs/transformers/en/installation#install-from-source
Generation: After installation, copy the code snippet below to run the example.
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
import torch
model_path="ibm-granite/granite-4.0-tiny-preview"
device="cuda"
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map=device,
torch_dtype=torch.bfloat16,
)
tokenizer = AutoTokenizer.from_pretrained(
model_path
)
conv = [{"role": "user", "content":"You have 10 liters of a 30% acid solution. How many liters of a 70% acid solution must be added to achieve a 50% acid mixture?"}]
input_ids = tokenizer.apply_chat_template(conv, return_tensors="pt", thinking=True, return_dict=True, add_generation_prompt=True).to(device)
set_seed(42)
output = model.generate(
**input_ids,
max_new_tokens=8192,
)
prediction = tokenizer.decode(output[0, input_ids["input_ids"].shape[1]:], skip_special_tokens=True)
print(prediction)
Evaluation Results:
Training Data: Overall, our training data is largely comprised of two key sources: (1) publicly available datasets with permissive license, (2) internal synthetically generated data targeted to enhance reasoning capabilities.
Infrastructure: We train Granite-4.0-Tiny-Preview 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-4.0-Tiny-Preview, leverages both permissively licensed open-source and select proprietary data for enhanced performance. Since it inherits its foundation from the previous model, all ethical considerations and limitations applicable to Granite-4.0-Tiny-Preview remain relevant.
Signature verification: Model signing is an experimental feature with ongoing development, which might include breaking changes. We are releasing these capabilities to improve the integrity of our models for our security-conscious users and to facilitate feedback from the community.
Before trying to verify the signature, ensure that the tensor files have been downloaded with git-lfs and that no files have been added, removed, or modified in your local git checkout:
git lfs fetch --all
git lfs pull
git lfs checkout
Install the model_signing (v1.0.1) library with the following command:
pip install 'model-signing==v1.1.1'
Then verify the signature with the following command ensuring that the IBM identity 'granite.preview@ibm.com' was used for signing this model:
python -m model_signing verify sigstore \
--signature model.sig \
--ignore-paths .git \
--ignore-paths .gitattributes \
--identity Granite.Preview@ibm.com \
--identity_provider https://sigstore.verify.ibm.com/oauth2 \
.
Resources
- ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite
- 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/
- 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources