3B long-context instruct model with RL alignment, IF, tool calling, and enterprise readiness.
5.1K
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.
| Attribute | Details |
|---|---|
| Provider | Granite Team, IBM |
| Architecture | granitehybrid |
| Cutoff date | Not disclosed |
| Languages | English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (extensible via finetuning) |
| Tool calling | ✅ |
| Input modalities | Text |
| Output modalities | Text |
| License | Apache 2.0 |
| Model variant | Parameters | Quantization | Context window | VRAM¹ | Size |
|---|---|---|---|---|---|
ai/granite-4.0-h-micro:3Bai/granite-4.0-h-micro:3B-Q4_K_Mai/granite-4.0-h-micro:latest | 3.2B | MOSTLY_Q4_K_M | 1M tokens | 2.32 GiB | 1.81 GB |
¹: VRAM estimated based on model characteristics.
latest→3B
docker model run ai/granite-4.0-h-micro
| Category | Metric | Granite-4.0-h-Micro |
|---|---|---|
| General Tasks | ||
| MMLU (5-shot) | 67.43 | |
| MMLU-Pro (5-shot, CoT) | 43.48 | |
| BBH (3-shot, CoT) | 69.36 | |
| AGI EVAL (0-shot, CoT) | 59.00 | |
| GPQA (0-shot, CoT) | 32.15 | |
| Alignment Tasks | ||
| AlpacaEval 2.0 | 31.49 | |
| IFEval (Instruct, Strict) | 86.94 | |
| IFEval (Prompt, Strict) | 81.71 | |
| IFEval (Average) | 84.32 | |
| ArenaHard | 36.15 | |
| Math Tasks | ||
| GSM8K (8-shot) | 81.35 | |
| GSM8K Symbolic (8-shot) | 77.50 | |
| Minerva Math (0-shot, CoT) | 66.44 | |
| DeepMind Math (0-shot, CoT) | 43.83 | |
| Code Tasks | ||
| HumanEval (pass@1) | 81.00 | |
| HumanEval+ (pass@1) | 75.00 | |
| MBPP (pass@1) | 73.00 | |
| MBPP+ (pass@1) | 64.00 | |
| CRUXEval-O (pass@1) | 41.25 | |
| BigCodeBench (pass@1) | 37.90 | |
| Tool Calling Tasks | ||
| BFCL v3 | 57.56 | |
| Multilingual Tasks | ||
| MULTIPLE (pass@1) | 49.46 | |
| MMMLU (5-shot) | 55.19 | |
| INCLUDE (5-shot) | 50.51 | |
| MGSM (8-shot) | 44.48 | |
| Safety | ||
| SALAD-Bench | 96.28 | |
| AttaQ | 84.44 |
Content type
Model
Digest
sha256:c12141a33…
Size
1.8 GB
Last updated
7 months ago
docker model pull ai/granite-4.0-h-microPulls:
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