onnx-community/granite-4.0-1b-ONNX-web

Granite-4.0-1B

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.

• Developers: Granite Team, IBM
• HF Collection: Granite 4.0 Nano Language Models HF Collection
• GitHub Repository: ibm-granite/granite-4.0-nano-language-models
• Website: Granite Docs
• Release Date: October 28, 2025
• License: Apache 2.0

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
General Tasks
MMLU	5-shot	35.01	36.21	59.39	59.74
MMLU-Pro	5-shot, CoT	12.13	14.38	34.02	32.86
BBH	3-shot, CoT	33.07	33.28	60.37	59.68
AGI EVAL	0-shot, CoT	26.22	29.61	49.22	52.44
GPQA	0-shot, CoT	24.11	26.12	29.91	29.69
Alignment Tasks
IFEval	Instruct, Strict	61.63	67.63	80.82	82.37
IFEval	Prompt, Strict	49.17	55.64	73.94	74.68
IFEval	Average	55.4	61.63	77.38	78.53
Math Tasks
GSM8K	8-shot	30.71	39.27	76.35	69.83
GSM Symbolic	8-shot	26.76	33.7	72.3	65.72
Minerva Math	0-shot, CoT	13.04	5.76	45.28	49.4
DeepMind Math	0-shot, CoT	8.45	6.2	34	34.98
Code Tasks
HumanEval	pass@1	39	38	74	73
HumanEval+	pass@1	37	35	69	68
MBPP	pass@1	48	49	65	69
MBPP+	pass@1	38	44	57	60
CRUXEval-O	pass@1	23.75	25.5	33.13	36
BigCodeBench	pass@1	11.14	11.23	30.18	29.12
Tool Calling Tasks
BFCL v3		39.32	43.32	54.82	50.21
Multilingual Tasks
MULTIPLE	pass@1	15.99	14.31	32.24	36.11
MMMLU	5-shot	28.23	27.95	45	49.43
INCLUDE	5-shot	27.74	27.09	42.12	43.35
MGSM	8-shot	14.72	16.16	37.84	27.52
Safety
SALAD-Bench		97.12	96.55	93.44	96.4
AttaQ		82.53	81.76	85.26	82.85

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
Embedding size	1024	768	2048	1536
Number of layers	28 attention	4 attention / 28 Mamba2	40 attention	4 attention / 36 Mamba2
Attention head size	64	64	128	128
Number of attention heads	16	12	16	12
Number of KV heads	4	4	4	4
Mamba2 state size	-	128	-	128
Number of Mamba2 heads	-	48	-	48
MLP / Shared expert hidden size	2048	2048	4096	4096
Num. Experts	-	-	-	-
Num. active Experts	-	-	-	-
Expert hidden size	-	-	-	-
MLP activation	SwiGLU	SwiGLU	SwiGLU	SwiGLU
Sequence length	32K	32K	128K	128K
Position embedding	RoPE	NoPE	RoPE	NoPE
# Parameters	350M	340M	1.6B	1.5B
# Active parameters	350M	340M	1.6B	1.5B

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.

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