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unsloth/
granite-4.0-h-tiny-FP8-Dynamic

Name: granite-4.0-h-tiny-FP8-Dynamic
Availability: InStock

ibm-granite/granite-4.0-h-tiny

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[!NOTE] Includes Unsloth chat template fixes!
For llama.cpp, use --jinja

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

Granite-4.0-H-Tiny

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

Generation: This is a simple example of how to use Granite-4.0-H-Tiny model.

Install the following libraries:

shell

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Then, copy the snippet from the section that is relevant for your use case.

python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.0-h-tiny"
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[0])

Expected output:

shell

<|start_of_role|>user<|end_of_role|>Please list one IBM Research laboratory located in the United States. You should only output its name and location.<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>Almaden Research Center, San Jose, California<|end_of_text|>

Tool-calling: Granite-4.0-H-Tiny comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of tools please follow OpenAI's function definition schema.

This is an example of how to use Granite-4.0-H-Tiny model tool-calling ability:

python

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather for a specified city.",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "Name of the city"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

# change input text as desired
chat = [
    { "role": "user", "content": "What's the weather like in Boston right now?" },
]
chat = tokenizer.apply_chat_template(chat, \
                                     tokenize=False, \
                                     tools=tools, \
                                     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[0])

Expected output:

shell

<|start_of_role|>system<|end_of_role|>You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.

You are provided with function signatures within <tools></tools> XML tags:
- <tools>
- unsloth
{"type": "function", "function": {"name": "get_current_weather", "description": "Get the current weather for a specified city.", "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "Name of the city"}}, "required": ["city"]}}}
</tools>

For each tool call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:
- <tool_call>
- unsloth
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.<|end_of_text|>
<|start_of_role|>user<|end_of_role|>What's the weather like in Boston right now?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|><tool_call>
{"name": "get_current_weather", "arguments": {"city": "Boston"}}
</tool_call><|end_of_text|>

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

unsloth/granite-4.0-h-tiny-FP8-Dynamic

Granite-4.0-H-Tiny

unsloth/
granite-4.0-h-tiny-FP8-Dynamic