granite-3.0-8b-instruct-GGUF

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Model creator: ibm-granite

Original model: granite-3.0-3b-a800m-instruct

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Email: [email protected]

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
• 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:

osllm.ai is a community-driven platform that provides access to a wide range of open-source language models.

1. 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.

2. 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.

3. IndoxGen: A framework for generating high-fidelity synthetic data using LLMs and
   human feedback, designed for enterprise use with high flexibility and precision.

4. 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.

5. 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 and the whole team working on llama.cpp for making all of this possible.

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