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Model Summary: Granite‑4.1‑30B‑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.
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-4.1-30B-Base model.
Install the following libraries:
Then, copy the code snippet below to run the example.
Expected output:
Evaluation Results:
Model Architecture:
Granite-4.1-30B-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.
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.
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-4.1-30B-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-4.1-30B-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
pip install torch torchvision torchaudiopip install acceleratepip install transformersfrom transformers import AutoModelForCausalLM, AutoTokenizerdevice = "cuda"
model_path = "ibm-granite/granite-4.1-30b-base"
tokenizer = AutoTokenizer.from_pretrained(model_path)# drop device_map if running on CPUmodel = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)model.eval()# change input text as desiredinput_text = "The capital of France is"# tokenize the textinput_tokens = tokenizer(input_text, return_tensors="pt").to(device)# generate output tokensoutput = model.generate(**input_tokens, max_length=10)# decode output tokens into textoutput = tokenizer.batch_decode(output)# print outputprint(output[0])The capital of France is Paris.