Granite-4.0-H-Tiny

💡 Designed for Deep Research Pipelines 💡

curl -X POST "http://localhost:3666/v1/chat/completions"
-H "Content-Type: application/json"
-d '{ "model": "granite-4-tiny", "messages": [ {"role": "user", "content": "Hello, how are you?"} ] }'

Model Summary: Granite-4.0-H-Tiny is a 7B parameter long-context instruct model finetuned from Granite-4.0-H-Tiny-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. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

• Developers: Granite Team, IBM
• HF Collection: Granite 4.0 Language Models HF Collection
• GitHub Repository: ibm-granite/granite-4.0-language-models
• Website: Granite Docs
• Release Date: October 2nd, 2025
• License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these languages.

Intended use: The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.

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

Evaluation Results:

Benchmarks	Metric	Micro Dense	H Micro Dense	H Tiny MoE	H Small MoE
General Tasks
MMLU	5-shot	65.98	67.43	68.65	78.44
MMLU-Pro	5-shot, CoT	44.5	43.48	44.94	55.47
BBH	3-shot, CoT	72.48	69.36	66.34	81.62
AGI EVAL	0-shot, CoT	64.29	59	62.15	70.63
GPQA	0-shot, CoT	30.14	32.15	32.59	40.63
Alignment Tasks
AlpacaEval 2.0		29.49	31.49	30.61	42.48
IFEval	Instruct, Strict	85.5	86.94	84.78	89.87
IFEval	Prompt, Strict	79.12	81.71	78.1	85.22
IFEval	Average	82.31	84.32	81.44	87.55
ArenaHard		25.84	36.15	35.75	46.48
Math Tasks
GSM8K	8-shot	85.45	81.35	84.69	87.27
GSM8K Symbolic	8-shot	79.82	77.5	81.1	87.38
Minerva Math	0-shot, CoT	62.06	66.44	69.64	74
DeepMind Math	0-shot, CoT	44.56	43.83	49.92	59.33
Code Tasks
HumanEval	pass@1	80	81	83	88
HumanEval+	pass@1	72	75	76	83
MBPP	pass@1	72	73	80	84
MBPP+	pass@1	64	64	69	71
CRUXEval-O	pass@1	41.5	41.25	39.63	50.25
BigCodeBench	pass@1	39.21	37.9	41.06	46.23
Tool Calling Tasks
BFCL v3		59.98	57.56	57.65	64.69
Multilingual Tasks
MULTIPLE	pass@1	49.21	49.46	55.83	57.37
MMMLU	5-shot	55.14	55.19	61.87	69.69
INCLUDE	5-shot	51.62	50.51	53.12	63.97
MGSM	8-shot	28.56	44.48	45.36	38.72
Safety
SALAD-Bench		97.06	96.28	97.77	97.3
AttaQ		86.05	84.44	86.61	86.64

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-H-Tiny baseline is built on a decoder-only MoE transformer architecture. Core components of this architecture are: GQA, Mamba2, MoEs with shared experts, SwiGLU activation, RMSNorm, and shared input/output embeddings.

Model	Micro Dense	H Micro Dense	H Tiny MoE	H Small MoE
Embedding size	2560	2048	1536	4096
Number of layers	40 attention	4 attention / 36 Mamba2	4 attention / 36 Mamba2	4 attention / 36 Mamba2
Attention head size	64	64	128	128
Number of attention heads	40	32	12	32
Number of KV heads	8	8	4	8
Mamba2 state size	-	128	128	128
Number of Mamba2 heads	-	64	48	128
MLP / Shared expert hidden size	8192	8192	1024	1536
Num. Experts	-	-	64	72
Num. active Experts	-	-	6	10
Expert hidden size	-	-	512	768
MLP activation	SwiGLU	SwiGLU	SwiGLU	SwiGLU
Sequence length	128K	128K	128K	128K
Position embedding	RoPE	NoPE	NoPE	NoPE
# Parameters	3B	3B	7B	32B
# Active parameters	3B	3B	1B	9B

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 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 Instruction 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