ibm-granite/granite-4.1-8b


Granite-4.1-8B

Model Summary: Granite-4.1-8B is a 8B parameter long-context instruct model finetuned from *Granite-4.1-8B-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-4.1-8B 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-4.1-8b"
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:

userPlease list one IBM Research laboratory located in the United States. You should only output its name and location.
assistantIBM Almaden Research Laboratory, San Jose, California, United States.

Tool-calling: Granite-4.1-8B 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-4.1-8B model tool-calling ability:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda"
model_path = "ibm-granite/granite-4.1-8b"
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:

systemYou 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.
userWhat's the weather like in Boston right now?
assistant
{"name": "get_current_weather", "arguments": {"city": "Boston"}}

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 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…