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Granite 3.0 2B Base by ibm-granite

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Granite 3.0 2B Base is an open-source language model by ibm-granite. Features: 2b LLM, VRAM: 10.1GB, Context: 4K, License: apache-2.0, LLM Explorer Score: 0.23.

  Arxiv:0000.00000   Granite   Granite-3.0   Language   Model-index   Region:us   Safetensors   Sharded   Tensorflow
Model Card on HF 🤗: https://huggingface.co/ibm-granite/granite-3.0-2b-base 

Granite 3.0 2B Base Benchmarks

MMLU Pro: 15.35
GPQA: 4.03
MUSR: 3.46
BBH: 17.56
IFEval: 38.74 vs 88 (so35)-56%
MATH Lvl 5: 5.44
LLME Score: 0.22799
nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").
Granite 3.0 2B Base (ibm-granite/granite-3.0-2b-base)
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Granite 3.0 2B Base Parameters and Internals

Model Type 
text-generation
Use Cases 
Areas:
text-to-text generation
Applications:
summarization, text classification, extraction, question-answering
Primary Use Cases:
baseline to create specialized models
Limitations:
Not undergone any safety alignment, may produce problematic outputs., Potential increased susceptibility to hallucination due to model size.
Considerations:
Community urged to use the model with ethical intentions.
Supported Languages 
English (supported), German (supported), Spanish (supported), French (supported), Japanese (supported), Portuguese (supported), Arabic (supported), Czech (supported), Italian (supported), Korean (supported), Dutch (supported), Chinese (supported)
Training Details 
Data Sources:
web, code, academic sources, books, math data, multilingual, instruction data
Data Volume:
12 trillion tokens for Stage 1 and 2 trillion tokens for Stage 2
Methodology:
Two-stage training strategy
Context Length:
4096
Hardware Used:
IBM's super computing cluster, Blue Vela, NVIDIA H100 GPUs
Model Architecture:
Decoder-only dense transformer architecture with GQA, RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings
Responsible Ai Considerations 
Fairness:
Involves awareness of bias and fairness.
Mitigation Strategies:
Ongoing research to address and mitigate issues.
Input Output 
Input Format:
Tokenized input using AutoTokenizer
Accepted Modalities:
text
Output Format:
Decodes output tokens into text using AutoTokenizer
Performance Tips:
Use appropriate libraries and follow examples provided.
LLM NameGranite 3.0 2B Base
Repository 🤗https://huggingface.co/ibm-granite/granite-3.0-2b-base 
Model Size2b
Required VRAM10.1 GB
Updated2026-03-29
Maintaineribm-granite
Model Typegranite
Model Files  5.0 GB: 1-of-3   4.9 GB: 2-of-3   0.2 GB: 3-of-3
Model ArchitectureGraniteForCausalLM
Licenseapache-2.0
Context Length4096
Model Max Length4096
Transformers Version4.45.0.dev0
Tokenizer ClassGPT2Tokenizer
Padding Token<|endoftext|>
Vocabulary Size49152

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Granite 3.1 2B Base128K / 5.1 GB250914
Granite 3.3 2B Base128K / 5.1 GB455722
Granite 3.3 2B Instruct128K / 5.1 GB3442
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... Instruct Literary Writer V0.1128K / 5.1 GB1012
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Granite Guardian 3.0 2B8K / 5.1 GB140721
Note: green Score (e.g. "73.2") means that the model is better than ibm-granite/granite-3.0-2b-base.

Rank the Granite 3.0 2B Base Capabilities

🆘 Have you tried this model? Rate its performance. This feedback would greatly assist ML community in identifying the most suitable model for their needs. Your contribution really does make a difference! 🌟

Instruction Following and Task Automation  
Factuality and Completeness of Knowledge  
Censorship and Alignment  
Data Analysis and Insight Generation  
Text Generation  
Text Summarization and Feature Extraction  
Code Generation  
Multi-Language Support and Translation  

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