--- base_model: - ibm-granite/granite-4.0-1b-base license: apache-2.0 library_name: transformers tags: - language - unsloth - granite-4.0 ---

Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants.

# Granite-4.0-1B-Base **Model Summary:** Granite-4.0-1B-Base is a lightweight decoder-only language model designed for scenarios where efficiency and speed are critical. They can run on resource-constrained devices such as smartphones or IoT hardware, enabling offline and privacy-preserving applications. It also supports Fill-in-the-Middle (FIM) code completion through the use of specialized prefix and suffix tokens. The model is trained from scratch on approximately 15 trillion tokens following a four-stage training strategy: 10 trillion tokens in the first stage, 2 trillion in the second, another 2 trillion in the third, and 0.5 trillion in the final stage. - **Developers:** Granite Team, IBM - **HF Collection:** [Granite 4.0 Nano Language Models HF Collection](https://huggingface.co/collections/ibm-granite/granite-40-nano-language-models-68e5775c80b60e43b72cfa16) - **GitHub Repository:** [ibm-granite/granite-4.0-nano-language-models](https://github.com/ibm-granite/granite-4.0-nano-language-models) - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) - **Release Date**: October 28, 2025 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fine-tune Granite 4.0 Nano models to support languages beyond those included in this list. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering and code-completion (including FIM) tasks. Moreover, these lightweight models can serve as baseline to create task-specific models for different applications. **Generation:** This is a simple example of how to use Granite-4.0-1B-Base model. Install the following libraries: ```shell pip install torch torchvision torchaudio pip install accelerate pip install transformers ``` Then, copy the code snippet below to run the example. ```python from transformers import AutoModelForCausalLM, AutoTokenizer device = "cuda" model_path = "ibm-granite/granite-4.0-1b-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: ```shell The capital of France is Paris. ``` **Evaluation Results:**
Benchmarks Metric 350M Dense H 350M Dense 1B Dense H 1B Dense
General Tasks
MMLU 5-shot 33.08 36.07 59.82 58.71
MMLU-Pro 5-shot,CoT 11.29 10.08 29.96 23.45
BBH 3-shot, CoT 32.19 29.96 57.73 48.45
AGI EVAL 3-shot 28.97 29.2 48.95 47.46
DROP 5-shot 29.77 28.56 58.18 57.18
Math Tasks
GSM8K 8-shot 24.11 24.41 62.4 57.39
Minerva Math 4-shot 9.96 11.5 30.3 21.3
Code Tasks
HumanEval pass@1 [StarCoder Prompt] 34.6 35.61 68.08 68.26
HumanEval pass@1 32 34 60 59
HumanEval+ pass@1 29 29 57 56
MBPP pass@1 45 17 72 65
MBPP+ pass@1 38 16 60 54
Multilingual Tasks
MMMLU 5-shot 30.93 31.02 46.73 48.55
INCLUDE 5-shot 27.32 29.26 42.6 43.8
MGSM 8-shot 13.92 15.12 46.96 41.52
Multilingual Benchmarks and thr 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-4.0-1B-Base is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
Model 350M Dense H 350M Dense 1B Dense H 1B Dense
Embedding size 1024 768 2048 1536
Number of layers 28 attention 4 attention / 28 Mamba2 40 attention 4 attention / 36 Mamba2
Attention head size 64 64 128 128
Number of attention heads 16 12 16 12
Number of KV heads 4 4 4 4
Mamba2 state size - 128 - 128
Number of Mamba2 heads - 48 - 48
MLP / Shared expert hidden size 2048 2048 4096 4096
Num. Experts - - - -
Num. active Experts - - - -
Expert hidden size - - - -
MLP activation SwiGLU SwiGLU SwiGLU SwiGLU
Sequence length 32K 32K 128K 128K
Position embedding RoPE NoPE RoPE NoPE
# Parameters 350M 340M 1.6B 1.5B
# Active parameters 350M 340M 1.6B 1.5B
**Training Data:** This model is trained on a mix of open source and proprietary data following a four-stage training strategy.
Stage Characteristics 350M Dense H 350M Dense 1B Dense H 1B Dense
I General mixture of training data, warmup, and power scheduler for learning rate. 10 10 10 10
II General mixture of training data with higher percentages of code and math with power scheduler for learning rate. 2 2 2 2
III High quality training data, exponential decay of learning rate. 2 2 2 2
IV High quality training data, linear decay to zero for learning rate. 0.5 0.5 0.5 0.5
**Infrastructure:** We trained the Granite 4.0 Nano 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-4.0-1B-Base model is not the exception in this regard. Even though this model is suited for multiple generative AI tasks, it has not undergone any safety alignment; therefore, 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-4.0-1B-Base model with ethical intentions and in a responsible way. **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/