README.md

---
base_model: ibm-granite/granite-3.0-3b-a800m-instruct
license: apache-2.0
pipeline_tag: text-generation
tags:
- language
- granite-3.0
quantized_by: bartowski
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: 42.49
      name: pass@1
    - type: pass@1
      value: 7.02
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: AGI-Eval
      type: human-exams
    metrics:
    - type: pass@1
      value: 25.7
      name: pass@1
    - type: pass@1
      value: 50.16
      name: pass@1
    - type: pass@1
      value: 20.51
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: OBQA
      type: commonsense
    metrics:
    - type: pass@1
      value: 40.8
      name: pass@1
    - type: pass@1
      value: 59.95
      name: pass@1
    - type: pass@1
      value: 71.86
      name: pass@1
    - type: pass@1
      value: 67.01
      name: pass@1
    - type: pass@1
      value: 48.0
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: BoolQ
      type: reading-comprehension
    metrics:
    - type: pass@1
      value: 78.65
      name: pass@1
    - type: pass@1
      value: 6.71
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: ARC-C
      type: reasoning
    metrics:
    - type: pass@1
      value: 50.94
      name: pass@1
    - type: pass@1
      value: 26.85
      name: pass@1
    - type: pass@1
      value: 37.7
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: HumanEvalSynthesis
      type: code
    metrics:
    - type: pass@1
      value: 39.63
      name: pass@1
    - type: pass@1
      value: 40.85
      name: pass@1
    - type: pass@1
      value: 35.98
      name: pass@1
    - type: pass@1
      value: 27.4
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: GSM8K
      type: math
    metrics:
    - type: pass@1
      value: 47.54
      name: pass@1
    - type: pass@1
      value: 19.86
      name: pass@1
  - task:
      type: text-generation
    dataset:
      name: PAWS-X (7 langs)
      type: multilingual
    metrics:
    - type: pass@1
      value: 50.23
      name: pass@1
    - type: pass@1
      value: 28.87
      name: pass@1
---
## 💫 Community Model> granite 3.0 3b a800m instruct by Ibm-Granite

*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.

**Model creator:** [ibm-granite](https://huggingface.co/ibm-granite)<br>
**Original model**: [granite-3.0-3b-a800m-instruct](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3930](https://github.com/ggerganov/llama.cpp/releases/tag/b3930)<br>

## Technical Details

This is an MoE model with 3b parameters and 800m activated parameters, making it blazing fast for inference.

Tuned on permissive open source datasets and internal synthetic datasets.

This model is designed to respond to general instructions and can be used to build AI assistants for multiple domains.

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese.

Context length of 4096 tokens.

## 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

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