ibm-granite/granite-4.1-8b · Hugging Face

Source: https://huggingface.co/ibm-granite/granite-4.1-8b

**Model Summary:**Granite-4.1-8B is a 8B parameter long-context instruct model finetuned fromGranite-4.1-8B-Baseusing 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:

<|start_of_role|>system<|end_of_role|>You are a helpful assistant. Please ensure responses are professional, accurate, and safe.<|end_of_text|>
<|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-4.1-8B comes with enhanced tool calling capabilities, enabling seamless integration with external functions and APIs. To define a list of tools please follow OpenAI’s functiondefinition 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:

<|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>
{"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>
{"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:

BenchmarksMetric3B Dense8B Dense30B DenseGeneral TasksMMLU5-shot67.0273.8480.16MMLU-Pro5-shot, CoT49.8355.9964.09BBH3-shot, CoT75.8380.5183.74AGI EVAL0-shot, CoT65.1672.4377.80GPQA0-shot, CoT31.7041.9645.76SimpleQA3.684.826.81Alignment TasksAlpacaEval 2.038.5750.0856.16IFEval Avg82.3085.8788.88ArenaHard37.8068.9871.02MTBench Avg7.578.618.61Math TasksGSM8K8-shot86.8892.4994.16GSM Symbolic8-shot81.3283.7075.70Minerva Math0-shot, CoT67.9480.1081.32DeepMind Math0-shot, CoT64.6480.0781.93Code [email protected][email protected]@[email protected]@[email protected]@152.5460.2662.31Eval+ [email protected] Calling TasksBFCL v360.8068.2773.68Multilingual TasksMMMLU5-shot57.6164.8473.71INCLUDE5-shot52.0558.8967.26MGSM8-shot70.0082.3271.12SafetySALAD-Bench93.9595.8096.41AttaQ81.8881.1985.76Tulu3 Safety Eval Avg66.8475.5778.19Multilingual Benchmarks and the included languages:Benchmarks# LangsLanguagesMMMLU11ar, de, en, es, fr, ja, ko, pt, zh, bn, hiINCLUDE14hi, bn, ta, te, ar, de, es, fr, it, ja, ko, nl, pt, zhMGSM5en, es, fr, ja, zhModel Architecture:

Granite-4.1-8B 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.

Model3B Dense8B Dense30B DenseEmbedding size256040964096Number of layers404064Attention head size64128128Number of attention heads403232Number of KV heads888MLP / Shared expert hidden size81921280032768MLP activationSwiGLUSwiGLUSwiGLUSequence length409640964096Position embeddingRoPERoPERoPE# Parameters3B8B30B**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-Tunning 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 rigorus SFT-RL cycles we have improved Granite-4.1 model’s tool calling, instruction following and chat capabilities. For further details please check ourGranite-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 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