Version

26.05.01

Calibration

STEM and Agentic

Languages

EN ZH HI AR RU

JA KO NL FR ES

Model Size

2.33 GB

Contact

![mof-class3-qualified](https://mot.isitopen.ai/model/1160)

Granite-4.1-3B

Model Summary:

Granite-4.1-3B is a 3B parameter long-context instruct model finetuned from Granite-4.1-3B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities.

Developers: Granite Team, IBM
HF Collection: Granite 4.1 Language Models HF Collection
Technical Blog: Granite-4.1 Blog
GitHub Repository: ibm-granite/granite-4.1-language-models
Website: Granite Docs
Release Date: April 29th, 2026
License: Apache 2.0

Supported Languages:

English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.

Intended use:

The model is designed to follow general instructions and can serve as the foundation for AI assistants across diverse domains, including business applications, as well as for LLM agents equipped with tool-use capabilities.

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-4.1-3B model.

Install the following libraries:

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.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.1-3b"
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:

<|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-4.1-3B 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-4.1-3B model tool-calling ability:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.1-3b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()

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:

<|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  XML tags:

{"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"]}}}

For each tool call, return a json object with function name and arguments within  XML tags:

{"name": , "arguments": }
. 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|>
{"name": "get_current_weather", "arguments": {"city": "Boston"}}
<|end_of_text|>

Evaluation Results:

Benchmarks

Metric

3B Dense

8B Dense

30B Dense

General Tasks

MMLU

5-shot

67.02

73.84

80.16

MMLU-Pro

5-shot, CoT

49.83

55.99

64.09

BBH

3-shot, CoT

75.83

80.51

83.74

AGI EVAL

0-shot, CoT

65.16

72.43

77.80

GPQA

0-shot, CoT

31.70

41.96

45.76

SimpleQA

3.68

4.82

6.81

Alignment Tasks

AlpacaEval 2.0

38.57

50.08

56.16

IFEval Avg

82.30

87.06

89.65

ArenaHard

37.80

68.98

71.02

MTBench Avg

7.57

8.61

Math Tasks

GSM8K

8-shot

86.88

92.49

94.16

GSM Symbolic

8-shot

81.32

83.70

75.70

Minerva Math

0-shot, CoT

67.94

80.10

81.32

DeepMind Math

0-shot, CoT

64.64

80.07

81.93

Code Tasks

HumanEval

pass@1

81.71

85.37

88.41

HumanEval+

pass@1

76.83

79.88

85.37

MBPP

pass@1

71.16

87.30

85.45

MBPP+

pass@1

62.17

73.81

73.54

CRUXEval-O

pass@1

40.75

47.63

55.75

BigCodeBench

pass@1

32.19

35.00

38.77

MULTIPLE

pass@1

52.54

60.26

62.31

Eval+ Avg

pass@1

67.05

80.21

82.66

Tool Calling Tasks

BFCL v3

60.80

68.27

73.68

Multilingual Tasks

MMMLU

5-shot

57.61

64.84

73.71

INCLUDE

5-shot

52.05

58.89

67.26

MGSM

8-shot

70.00

82.32

71.12

Safety

SALAD-Bench

93.95

95.80

96.41

AttaQ

81.88

81.19

85.76

Tulu3 Safety Eval Avg

66.84

75.57

78.19

Multilingual Benchmarks and the included languages:

Benchmarks

# Langs

Languages

MMMLU

ar, de, en, es, fr, ja, ko, pt, zh, bn, hi

INCLUDE

hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zh

MGSM

en, es, fr, ja, zh

Model Architecture:

Granite-4.1-3B baseline is built on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.

Model

3B Dense

8B Dense

30B Dense

Embedding size

2560

4096

Number of layers

Attention head size

128

Number of attention heads

Number of KV heads

MLP / Shared expert hidden size

8192

12800

32768

MLP activation

SwiGLU

Sequence length

131072

Position embedding

RoPE

# Parameters

30B

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.

Supervised Fine-Tuning and Reinforcement Learning:

Instruct model has been fine tuned with significantly improved SFT-pipeline and Reinforcement learning pipelines with high quality mix of various datasets as mentioned above. With rigorous SFT-RL cycles we have improved Granite-4.1 model's tool calling, instruction following and chat capabilities. For further details please check our Granite-4.1 Blog.

Infrastructure:

We trained the Granite 4.1 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 4.1 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 cases, 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. We urge the community to use this model with proper safety testing and tuning tailored for their specific tasks. To enhance safety in enterprise deployments, we recommend using Granite 4.1 Language models alongside Granite Guardian, a model designed to detect and flag risks in inputs and outputs across key dimensions outlined in the IBM AI Risk Atlas.

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

cyankiwi/granite-4.1-3b-AWQ-INT4

Granite-4.1-3B