Granite-4.1-8B-Base

Model Summary: Granite‑4.1‑8B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of text‑to‑text generation tasks. In addition to standard generation, it supports Fill‑in‑the‑Middle (FIM) code completion through specialized prefix and suffix tokens. The model is trained from scratch on approximately 15 trillion tokens using a five‑phase training strategy: 10 trillion tokens in phase one, 2 trillion tokens each in phases two and three, and 0.5 trillion tokens in phase four. In the final phase, long‑context extension is applied to expand the model’s context window to 512K tokens.

• 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: Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, code-completion (including FIM), and long-context generation tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.

Generation: This is a simple example of how to use Granite-4.1-8B-Base model.

Install the following libraries:

pip install torch torchvision torchaudio
pip install accelerate
pip install transformers

Then, copy the code snippet below to run the example.

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda"

model_path = "ibm-granite/granite-4.1-8b-base"

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
input_text = "The capital of France is"
# tokenize the text
input_tokens = tokenizer(input_text, return_tensors="pt").to(device)
# generate output tokens
output = model.generate(**input_tokens, max_length=10)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# print output
print(output[0])

Expected output:

The capital of France is Paris.

Evaluation Results:

Benchmarks	Metric	3B Dense	8B Dense	30B Dense
General Tasks
MMLU	5-shot	66.47	73.60	78.44
MMLU-Pro	5-shot,CoT	37.16	44.58	49.51
BBH	3-shot, CoT	63.84	73.83	80.66
AGI EVAL	3-shot	54.32	61.68	69.20
DROP	5-shot	66.04	72.36	78.57
SimpleQA	no-judge-short-form	6.85	7.92	10.54
Math Tasks
GSM8K	8-shot	72.93	73.54	83.78
Minerva Math	4-shot	38.00	43.42	45.66
Code Tasks
HumanEval	pass@1 [StarCoder Prompt]	76.19	79.24	81.52
HumanEval	pass@1	59.76	68.29	67.68
HumanEval+	pass@1	54.27	62.80	62.20
MBPP	pass@1	81.48	63.76	83.60
MBPP+	pass@1	68.25	53.97	69.58
Eval+ Avg		65.94	62.21	70.76
Multilingual Tasks
MMMLU	5-shot	56.59	64.73	73.36
INCLUDE	5-shot	51.77	57.60	67.07
MGSM	8-shot	58.48	63.68	74.40

Multilingual Benchmarks and the 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-4.1-8B-Base is based 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	4096
Number of layers	40	40	64
Attention head size	64	128	128
Number of attention heads	40	32	32
Number of KV heads	8	8	8
MLP / Shared expert hidden size	8192	12800	32768
MLP activation	SwiGLU	SwiGLU	SwiGLU
Sequence length	131072	131072	131072
Position embedding	RoPE	RoPE	RoPE
# Parameters	3B	8B	30B

Training Data: This model is trained on a mix of open source and proprietary data following a five-phase training strategy. We refer to phase-1 and phase-2 as pre-training and phase-3, phase-4, and phase-5 as mid-training.

Stage	Characteristics	3B Dense	8B Dense	30B Dense
I	General mixture of training data, warmup, and power scheduler for learning rate.	10T	10T	10T
II	General mixture of training data with higher percentages of code and math with power scheduler for learning rate.	2T	2T	2T
III	High quality training data, exponential decay of learning rate.	2T	2T	2T
IV	High quality training data, linear decay to zero for learning rate.	500B	500B	500B
V	Long Context Extension with exponential learning rate schedule.	396B	396B	396B

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: The use of Large Language Models involves risks and ethical considerations people must be aware of, including but not limited to: bias and fairness, misinformation, and autonomous decision-making. Granite-4.1-8B-Base model is not an exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment and it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying text verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use Granite-4.1-8B-Base model with ethical intentions and in a responsible way. 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://github.com/ibm-granite-community/
• PRISM: Demystifying Retention and Interaction in Mid-Training