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ibm-granite_granite-4.0-h-small
Granite-4.0-H-Small is a 32B parameter long-context instruct model finetuned from Granite-4.0-H-Small-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

Repository: localaiLicense: apache-2.0

ibm-granite_granite-4.0-h-tiny
Granite-4.0-H-Tiny is a 7B parameter long-context instruct model finetuned from Granite-4.0-H-Tiny-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

Repository: localaiLicense: apache-2.0

ibm-granite_granite-4.0-h-micro
Granite-4.0-H-Micro is a 3B parameter long-context instruct model finetuned from Granite-4.0-H-Micro-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

Repository: localaiLicense: apache-2.0

ibm-granite_granite-4.0-micro
Granite-4.0-Micro is a 3B parameter long-context instruct model finetuned from Granite-4.0-Micro-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved instruction following (IF) and tool-calling capabilities, making them more effective in enterprise applications.

Repository: localaiLicense: apache-2.0

ibm-granite.granite-4.0-1b
### **Granite-4.0-1B** *By IBM | Apache 2.0 License* **Overview:** Granite-4.0-1B is a lightweight, instruction-tuned language model designed for efficient on-device and research use. Built on a decoder-only dense transformer architecture, it delivers strong performance in instruction following, code generation, tool calling, and multilingual tasks—making it ideal for applications requiring low latency and minimal resource usage. **Key Features:** - **Size:** 1.6 billion parameters (1B Dense), optimized for efficiency. - **Capabilities:** - Text generation, summarization, question answering - Code completion and function calling (e.g., API integration) - Multilingual support (English, Spanish, French, German, Japanese, Chinese, Arabic, Korean, Portuguese, Italian, Dutch, Czech) - Robust safety and alignment via instruction tuning and reinforcement learning - **Architecture:** Uses GQA (Grouped Query Attention), SwiGLU activation, RMSNorm, shared input/output embeddings, and RoPE position embeddings. - **Context Length:** Up to 128K tokens — suitable for long-form content and complex reasoning. - **Training:** Finetuned from *Granite-4.0-1B-Base* using open-source datasets, synthetic data, and human-curated instruction pairs. **Performance Highlights (1B Dense):** - **MMLU (5-shot):** 59.39 - **HumanEval (pass@1):** 74 - **IFEval (Alignment):** 80.82 - **GSM8K (8-shot):** 76.35 - **SALAD-Bench (Safety):** 93.44 **Use Cases:** - On-device AI applications - Research and prototyping - Fine-tuning for domain-specific tasks - Low-resource environments with high performance expectations **Resources:** - [Hugging Face Model](https://huggingface.co/ibm-granite/granite-4.0-1b) - [Granite Docs](https://www.ibm.com/granite/docs/) - [GitHub Repository](https://github.com/ibm-granite/granite-4.0-nano-language-models) > *“Make knowledge free for everyone.” – IBM Granite Team*

Repository: localaiLicense: apache-2.0