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granite-3.0-3b-a800m-instruct-GGUF

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metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - language
  - granite-3.0
  - TensorBlock
  - GGUF
base_model: ibm-granite/granite-3.0-3b-a800m-instruct
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
            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
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ibm-granite/granite-3.0-3b-a800m-instruct - GGUF

This repo contains GGUF format model files for ibm-granite/granite-3.0-3b-a800m-instruct.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Prompt template 

<|start_of_role|>system<|end_of_role|>{system_prompt}<|end_of_text|>
<|start_of_role|>user<|end_of_role|>{prompt}<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>

Model file specification

Filename Quant type File Size Description
granite-3.0-3b-a800m-instruct-Q2_K.gguf Q2_K 1.266 GB smallest, significant quality loss - not recommended for most purposes
granite-3.0-3b-a800m-instruct-Q3_K_S.gguf Q3_K_S 1.489 GB very small, high quality loss
granite-3.0-3b-a800m-instruct-Q3_K_M.gguf Q3_K_M 1.644 GB very small, high quality loss
granite-3.0-3b-a800m-instruct-Q3_K_L.gguf Q3_K_L 1.774 GB small, substantial quality loss
granite-3.0-3b-a800m-instruct-Q4_0.gguf Q4_0 1.926 GB legacy; small, very high quality loss - prefer using Q3_K_M
granite-3.0-3b-a800m-instruct-Q4_K_S.gguf Q4_K_S 1.942 GB small, greater quality loss
granite-3.0-3b-a800m-instruct-Q4_K_M.gguf Q4_K_M 2.059 GB medium, balanced quality - recommended
granite-3.0-3b-a800m-instruct-Q5_0.gguf Q5_0 2.338 GB legacy; medium, balanced quality - prefer using Q4_K_M
granite-3.0-3b-a800m-instruct-Q5_K_S.gguf Q5_K_S 2.338 GB large, low quality loss - recommended
granite-3.0-3b-a800m-instruct-Q5_K_M.gguf Q5_K_M 2.407 GB large, very low quality loss - recommended
granite-3.0-3b-a800m-instruct-Q6_K.gguf Q6_K 2.776 GB very large, extremely low quality loss
granite-3.0-3b-a800m-instruct-Q8_0.gguf Q8_0 3.593 GB very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/granite-3.0-3b-a800m-instruct-GGUF --include "granite-3.0-3b-a800m-instruct-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/granite-3.0-3b-a800m-instruct-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'