base_model: ibm-granite/granite-3.0-2b-instruct
license: apache-2.0
pipeline_tag: text-generation
tags:
- language
- granite-3.0
quantized_model: AliNemati
inference: false
model-index:
- name: granite-3.0-2b-instruct
results:
- task:
type: text-generation
dataset:
name: IFEval
type: instruction-following
metrics:
- type: pass@1
value: 52.27
name: pass@1
- type: pass@1
value: 8.22
name: pass@1
- task:
type: text-generation
dataset:
name: AGI-Eval
type: human-exams
metrics:
- type: pass@1
value: 40.52
name: pass@1
- type: pass@1
value: 65.82
name: pass@1
- type: pass@1
value: 34.45
name: pass@1
- task:
type: text-generation
dataset:
name: OBQA
type: commonsense
metrics:
- type: pass@1
value: 46.6
name: pass@1
- type: pass@1
value: 71.21
name: pass@1
- type: pass@1
value: 82.61
name: pass@1
- type: pass@1
value: 77.51
name: pass@1
- type: pass@1
value: 60.32
name: pass@1
- task:
type: text-generation
dataset:
name: BoolQ
type: reading-comprehension
metrics:
- type: pass@1
value: 88.65
name: pass@1
- type: pass@1
value: 21.58
name: pass@1
- task:
type: text-generation
dataset:
name: ARC-C
type: reasoning
metrics:
- type: pass@1
value: 64.16
name: pass@1
- type: pass@1
value: 33.81
name: pass@1
- type: pass@1
value: 51.55
name: pass@1
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis
type: code
metrics:
- type: pass@1
value: 64.63
name: pass@1
- type: pass@1
value: 57.16
name: pass@1
- type: pass@1
value: 65.85
name: pass@1
- type: pass@1
value: 49.6
name: pass@1
- task:
type: text-generation
dataset:
name: GSM8K
type: math
metrics:
- type: pass@1
value: 68.99
name: pass@1
- type: pass@1
value: 30.94
name: pass@1
- task:
type: text-generation
dataset:
name: PAWS-X (7 langs)
type: multilingual
metrics:
- type: pass@1
value: 64.94
name: pass@1
- type: pass@1
value: 48.2
name: pass@1
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Model creator: ibm-granite
Original model: granite-3.0-3b-a800m-instruct
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Model Summary: Granite-3.0-2B-Instruct is a 2B parameter model finetuned from Granite-3.0-2B-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.
- Developers: Granite Team, IBM
- GitHub Repository: ibm-granite/granite-3.0-language-models
- Website: Granite Docs
- Paper: Granite 3.0 Language Models
- Release Date: October 21st, 2024
- License: Apache 2.0
Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages.
Intended use: The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
Capabilities
- Summarization
- Text classification
- Text extraction
- Question-answering
- Retrieval Augmented Generation (RAG)
- Code related tasks
- Function-calling tasks
- Multilingual dialog use cases
About osllm.ai:
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IndoxJudge: A free, open-source tool for evaluating large language models (LLMs).
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online and offline LLMs.IndoxGen: A framework for generating high-fidelity synthetic data using LLMs and
human feedback, designed for enterprise use with high flexibility and precision.Phoenix: A multi-platform, open-source chatbot that interacts with documents
locally, without internet or GPU. It integrates inDox and IndoxJudge to improve accuracy and prevent hallucinations,
ideal for sensitive fields like healthcare.Phoenix_cli: A multi-platform command-line tool that runs LLaMA models locally,
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