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--- |
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base_model: ibm-granite/granite-3.0-2b-instruct |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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tags: |
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- language |
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- granite-3.0 |
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quantized_model: AliNemati |
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inference: false |
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model-index: |
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- name: granite-3.0-2b-instruct |
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results: |
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- task: |
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type: text-generation |
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dataset: |
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name: IFEval |
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type: instruction-following |
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metrics: |
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- type: pass@1 |
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value: 52.27 |
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name: pass@1 |
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- type: pass@1 |
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value: 8.22 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: AGI-Eval |
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type: human-exams |
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metrics: |
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- type: pass@1 |
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value: 40.52 |
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name: pass@1 |
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- type: pass@1 |
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value: 65.82 |
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name: pass@1 |
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- type: pass@1 |
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value: 34.45 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: OBQA |
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type: commonsense |
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metrics: |
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- type: pass@1 |
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value: 46.6 |
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name: pass@1 |
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- type: pass@1 |
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value: 71.21 |
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name: pass@1 |
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- type: pass@1 |
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value: 82.61 |
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name: pass@1 |
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- type: pass@1 |
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value: 77.51 |
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name: pass@1 |
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- type: pass@1 |
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value: 60.32 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: BoolQ |
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type: reading-comprehension |
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metrics: |
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- type: pass@1 |
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value: 88.65 |
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name: pass@1 |
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- type: pass@1 |
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value: 21.58 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: ARC-C |
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type: reasoning |
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metrics: |
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- type: pass@1 |
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value: 64.16 |
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name: pass@1 |
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- type: pass@1 |
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value: 33.81 |
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name: pass@1 |
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- type: pass@1 |
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value: 51.55 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: HumanEvalSynthesis |
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type: code |
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metrics: |
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- type: pass@1 |
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value: 64.63 |
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name: pass@1 |
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- type: pass@1 |
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value: 57.16 |
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name: pass@1 |
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- type: pass@1 |
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value: 65.85 |
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name: pass@1 |
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- type: pass@1 |
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value: 49.6 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: GSM8K |
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type: math |
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metrics: |
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- type: pass@1 |
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value: 68.99 |
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name: pass@1 |
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- type: pass@1 |
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value: 30.94 |
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name: pass@1 |
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- task: |
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type: text-generation |
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dataset: |
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name: PAWS-X (7 langs) |
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type: multilingual |
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metrics: |
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- type: pass@1 |
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value: 64.94 |
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name: pass@1 |
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- type: pass@1 |
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value: 48.2 |
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name: pass@1 |
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--- |
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**osllm.ai Models Highlights Program** |
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**We believe there's no need to pay a token if you have a GPU on your computer.** |
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Highlighting new and noteworthy models from the community. Join the conversation on Discord. |
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**Model creator**: ibm-granite |
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**Original model**: granite-3.0-3b-a800m-instruct |
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[**README**:](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct/edit/main/README.md) |
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<p align="center"> |
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<a href="https://osllm.ai">Official Website</a> • <a href="https://docs.osllm.ai/index.html">Documentation</a> • <a href="https://discord.gg/2fftQauwDD">Discord</a> |
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</p> |
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Email: support@osllm.ai |
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**Model Summary:** |
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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. |
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- **Developers:** Granite Team, IBM |
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- **GitHub Repository:** [ibm-granite/granite-3.0-language-models](https://github.com/ibm-granite/granite-3.0-language-models) |
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- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) |
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- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf) |
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- **Release Date**: October 21st, 2024 |
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- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) |
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**Supported Languages:** |
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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. |
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**Intended use:** |
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The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications. |
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*Capabilities* |
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* Summarization |
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* Text classification |
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* Text extraction |
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* Question-answering |
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* Retrieval Augmented Generation (RAG) |
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* Code related tasks |
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* Function-calling tasks |
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* Multilingual dialog use cases |
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**About [osllm.ai](https://osllm.ai)**: |
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[osllm.ai](https://osllm.ai) is a community-driven platform that provides access to a wide range of open-source language models. |
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1. **[IndoxJudge](https://github.com/indoxJudge)**: A free, open-source tool for evaluating large language models (LLMs). |
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It provides key metrics to assess performance, reliability, and risks like bias and toxicity, helping ensure model safety. |
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1. **[inDox](https://github.com/inDox)**: An open-source retrieval augmentation tool for extracting data from various |
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document formats (text, PDFs, HTML, Markdown, LaTeX). It handles structured and unstructured data and supports both |
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online and offline LLMs. |
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1. **[IndoxGen](https://github.com/IndoxGen)**: A framework for generating high-fidelity synthetic data using LLMs and |
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human feedback, designed for enterprise use with high flexibility and precision. |
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1. **[Phoenix](https://github.com/Phoenix)**: A multi-platform, open-source chatbot that interacts with documents |
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locally, without internet or GPU. It integrates inDox and IndoxJudge to improve accuracy and prevent hallucinations, |
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ideal for sensitive fields like healthcare. |
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1. **[Phoenix_cli](https://github.com/Phoenix_cli)**: A multi-platform command-line tool that runs LLaMA models locally, |
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supporting up to eight concurrent tasks through multithreading, eliminating the need for cloud-based services. |
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**Special thanks** |
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🙏 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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