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README.md

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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**
+
+**We believe there's no need to pay a token if you have a GPU on your computer.**
+
+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
+
+
+<p align="center">
+  <a href="https://osllm.ai">Official Website</a> &bull; <a href="https://docs.osllm.ai/index.html">Documentation</a> &bull; <a href="https://discord.gg/2fftQauwDD">Discord</a>
+</p>
+
+
+
+<p align="center">
+  <b>NEW:</b> <a href="https://docs.google.com/forms/d/1CQXJvxLUqLBSXnjqQmRpOyZqD6nrKubLz2WTcIJ37fU/prefill">Subscribe to our mailing list</a> for updates and news!
+</p>
+
+
+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**:
+
+This model has been meticulously fine-tuned using a combination of permissive open source datasets along with internally generated synthetic datasets to enhance its capabilities.  
+
+
+It is specifically crafted to effectively respond to a wide array of general instructions, making it highly versatile for the creation of AI assistants that can operate across various domains and applications.  
+
+
+**Languages Supported:** This powerful model is proficient in multiple languages, including but not limited to English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese, ensuring comprehensive accessibility for a diverse user base.
+
+**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://example.com)
+
+**Paper**: [Granite 3.0 Language Models](https://example.com)
+
+**Release Date**: October 21st, 2024
+
+**License**: Apache 2.0
+
+
+
+**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.
+
+
+
+**Disclaimers**
+
+[osllm.ai](https://osllm.ai) is not the creator, originator, or owner of any Model featured in the Community Model Program.  
+Each Community Model is created and provided by third parties. osllm.ai does not endorse, support, represent,  
+or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand  
+that Community Models can produce content that might be offensive, harmful, inaccurate, or otherwise  
+inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who  
+originated such Model. osllm.ai may not monitor or control the Community Models and cannot, and does not, take  
+responsibility for any such Model. osllm.ai disclaims all warranties or guarantees about the accuracy,  
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+
+
+
+