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ai/granite-4.0-h-micro

Verified Publisher

By Docker

•Updated about 1 year ago

3B long-context instruct model with RL alignment, IF, tool calling, and enterprise readiness.

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ai/granite-4.0-h-micro repository overview

⁠Granite-4.0-h-Micro

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⁠Description

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.

⁠Characteristics

AttributeDetails
ProviderGranite Team, IBM
Architecturegranitehybrid
Cutoff dateNot disclosed
LanguagesEnglish, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, Chinese (extensible via finetuning)
Tool calling✅
Input modalitiesText
Output modalitiesText
LicenseApache 2.0

⁠Available model variants

Model variantParametersQuantizationContext windowVRAM¹Size
ai/granite-4.0-h-micro:3B

ai/granite-4.0-h-micro:3B-Q4_K_M

ai/granite-4.0-h-micro:latest
3.2BMOSTLY_Q4_K_M1M tokens2.32 GiB1.81 GB

¹: VRAM estimated based on model characteristics.

latest → 3B

⁠Use this AI model with Docker Model Runner

docker model run ai/granite-4.0-h-micro

⁠Considerations

  • Optimized for instruction following, tool/function calling, and long-context (up to 128K tokens) scenarios.
  • Strong generalist capabilities: summarization, classification, extraction, QA/RAG, coding, function-calling, and multilingual dialogue.
  • Multilingual: best performance in English; a few-shot approach or light finetuning can help close gaps for other languages.
  • Safety & reliability: despite alignment, the model can still produce inaccurate or biased outputs—apply domain-specific evaluation and guardrails.
  • Infrastructure note: trained on NVIDIA GB200 NVL72 at CoreWeave; use acceleration libraries (e.g., accelerate, optimized attention/KV cache settings) for efficient inference.

⁠Benchmark performance

CategoryMetricGranite-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 2.031.49
IFEval (Instruct, Strict)86.94
IFEval (Prompt, Strict)81.71
IFEval (Average)84.32
ArenaHard36.15
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 v357.56
Multilingual Tasks
MULTIPLE (pass@1)49.46
MMMLU (5-shot)55.19
INCLUDE (5-shot)50.51
MGSM (8-shot)44.48
Safety
SALAD-Bench96.28
AttaQ84.44

Tag summary

Content type

Model

Digest

sha256:c12141a33…

Size

1.8 GB

Last updated

about 1 year ago

docker model pull ai/granite-4.0-h-micro

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