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---
base_model: ibm-granite/granite-3.0-8b-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
---
**osllm.ai Models Highlights Program**
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Highlighting new and noteworthy models from the community. Join the conversation on Discord.
**Model creator**: ibm-granite
**Original model**: granite-3.0-3b-a800m-instruct
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**Model Summary**:
Granite-3.0-8B-Instruct is an 8B parameter model finetuned from Granite-3.0-8B-Base using a combination of open-source instruction datasets with permissive licenses 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.
**Technical Specifications**:
# Granite-3.0-8B-Instruct
**Model Summary:**
Granite-3.0-8B-Instruct is a 8B parameter model finetuned from *Granite-3.0-8B-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](https://github.com/ibm-granite/granite-3.0-language-models)
- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf)
- **Release Date**: October 21st, 2024
- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-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](https://osllm.ai)**:
[osllm.ai](https://osllm.ai) is a community-driven platform that provides access to a wide range of open-source language models.
1. **[IndoxJudge](https://github.com/indoxJudge)**: A free, open-source tool for evaluating large language models (LLMs).
It provides key metrics to assess performance, reliability, and risks like bias and toxicity, helping ensure model safety.
1. **[inDox](https://github.com/inDox)**: An open-source retrieval augmentation tool for extracting data from various
document formats (text, PDFs, HTML, Markdown, LaTeX). It handles structured and unstructured data and supports both
online and offline LLMs.
1. **[IndoxGen](https://github.com/IndoxGen)**: A framework for generating high-fidelity synthetic data using LLMs and
human feedback, designed for enterprise use with high flexibility and precision.
1. **[Phoenix](https://github.com/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.
1. **[Phoenix_cli](https://github.com/Phoenix_cli)**: A multi-platform command-line tool that runs LLaMA models locally,
supporting up to eight concurrent tasks through multithreading, eliminating the need for cloud-based services.
**Special thanks**
🙏 Special thanks to [**Georgi Gerganov**](https://github.com/ggerganov) and the whole team working on [**llama.cpp**](https://github.com/ggerganov/llama.cpp) for making all of this possible.
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