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metadata
pipeline_tag: text-generation
inference: false
license: apache-2.0
library_name: transformers
tags:
  - language
  - granite-3.0
  - llama-cpp
  - gguf-my-repo
base_model: ibm-granite/granite-3.0-1b-a400m-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: 32.39
            name: pass@1
          - type: pass@1
            value: 6.17
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: AGI-Eval
          type: human-exams
        metrics:
          - type: pass@1
            value: 20.35
            name: pass@1
          - type: pass@1
            value: 32
            name: pass@1
          - type: pass@1
            value: 12.21
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: OBQA
          type: commonsense
        metrics:
          - type: pass@1
            value: 38.4
            name: pass@1
          - type: pass@1
            value: 47.55
            name: pass@1
          - type: pass@1
            value: 65.59
            name: pass@1
          - type: pass@1
            value: 61.17
            name: pass@1
          - type: pass@1
            value: 49.11
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: BoolQ
          type: reading-comprehension
        metrics:
          - type: pass@1
            value: 70.12
            name: pass@1
          - type: pass@1
            value: 1.27
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: ARC-C
          type: reasoning
        metrics:
          - type: pass@1
            value: 41.21
            name: pass@1
          - type: pass@1
            value: 23.07
            name: pass@1
          - type: pass@1
            value: 31.77
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: HumanEvalSynthesis
          type: code
        metrics:
          - type: pass@1
            value: 30.18
            name: pass@1
          - type: pass@1
            value: 26.22
            name: pass@1
          - type: pass@1
            value: 21.95
            name: pass@1
          - type: pass@1
            value: 15.4
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: GSM8K
          type: math
        metrics:
          - type: pass@1
            value: 26.31
            name: pass@1
          - type: pass@1
            value: 10.88
            name: pass@1
      - task:
          type: text-generation
        dataset:
          name: PAWS-X (7 langs)
          type: multilingual
        metrics:
          - type: pass@1
            value: 45.84
            name: pass@1
          - type: pass@1
            value: 11.8
            name: pass@1

davelsphere/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF

This model was converted to GGUF format from ibm-granite/granite-3.0-1b-a400m-instruct using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo davelsphere/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo davelsphere/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo davelsphere/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo davelsphere/granite-3.0-1b-a400m-instruct-Q4_K_M-GGUF --hf-file granite-3.0-1b-a400m-instruct-q4_k_m.gguf -c 2048