Model Summary: Granite-4.0-1B is a lightweight instruct model finetuned from Granite-4.0-1B-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 including supervised finetuning, reinforcement learning, and model merging.
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fine-tune Granite 4.0 Nano models to support languages beyond those included in this list.
Intended use: Granite 4.0 Nano instruct models feature strong instruction following capabilities bringing advanced AI capabilities within reach for on-device deployments and research use cases. Additionally, their compact size makes them well-suited for fine-tuning on specialized domains without requiring massive compute resources.
Capabilities
Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases
Fill-In-the-Middle (FIM) code completions
Generation:
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Basic example
import { pipeline, TextStreamer } from "@huggingface/transformers";
// Create a text generation pipeline
const generator = await pipeline(
"text-generation",
"onnx-community/granite-4.0-1b-ONNX",
{ device: "webgpu", dtype: "q4" },
);
// Define the list of messages
const messages = [
{ role: "system", content: "You are a helpful assistant." },
{ role: "user", content: "What is the capital of France?" },
];
// Generate a response
const output = await generator(messages, {
max_new_tokens: 512,
do_sample: false,
streamer: new TextStreamer(generator.tokenizer, { skip_prompt: true, skip_special_tokens: true }),
});
console.log(output[0].generated_text.at(-1).content);
// The capital of France is Paris.
Evaluation Results:
| Benchmarks | Metric | 350M Dense | H 350M Dense | 1B Dense | H 1B Dense
Multilingual Benchmarks and thr included languages:
| Benchmarks | # Langs | Languages
| MMMLU | 11 | ar, de, en, es, fr, ja, ko, pt, zh, bn, hi
| INCLUDE | 14 | hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh
| MGSM | 5 | en, es, fr, ja, zh
Model Architecture:
Granite-4.0-1B baseline is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
| Model | 350M Dense | H 350M Dense | 1B Dense | H 1B Dense
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) a select set of human-curated data.
Infrastructure: We trained the Granite 4.0 Nano Language Models utilizing an NVIDIA GB200 NVL72 cluster hosted in CoreWeave. Intra-rack communication occurs via the 72-GPU NVLink domain, and a non-blocking, full Fat-Tree NDR 400 Gb/s InfiniBand network provides inter-rack communication. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs.
Ethical Considerations and Limitations: Granite 4.0 Nano Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering multiple 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.
onnx-community <huggingface.co/onnx-community>Newsgroups: alt.freeweights.graniteSubject: 
