metadata

pipeline_tag: text-generation
inference: false
license: apache-2.0
datasets:
  - bigcode/commitpackft
  - TIGER-Lab/MathInstruct
  - meta-math/MetaMathQA
  - glaiveai/glaive-code-assistant-v3
  - glaive-function-calling-v2
  - bugdaryan/sql-create-context-instruction
  - garage-bAInd/Open-Platypus
  - nvidia/HelpSteer
  - bigcode/self-oss-instruct-sc2-exec-filter-50k
metrics:
  - code_eval
library_name: transformers
tags:
  - code
  - granite
  - llama-cpp
  - gguf-my-repo
base_model: ibm-granite/granite-3b-code-instruct-128k
model-index:
  - name: granite-3b-code-instruct-128k
    results:
      - task:
          type: text-generation
        dataset:
          name: HumanEvalSynthesis (Python)
          type: bigcode/humanevalpack
        metrics:
          - type: pass@1
            value: 53.7
            name: pass@1
            verified: false
          - type: pass@1
            value: 41.4
            name: pass@1
            verified: false
          - type: pass@1
            value: 25.1
            name: pass@1
            verified: false
          - type: pass@1
            value: 26.2
            name: pass@1
            verified: false
      - task:
          type: text-generation
        dataset:
          name: RepoQA (Python@16K)
          type: repoqa
        metrics:
          - type: pass@1 (thresh=0.5)
            value: 48
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 36
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 38
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 39
            name: pass@1 (thresh=0.5)
            verified: false
          - type: pass@1 (thresh=0.5)
            value: 29
            name: pass@1 (thresh=0.5)
            verified: false

AIronMind/granite-3b-code-instruct-128k-Q4_K_M-GGUF

This model was converted to GGUF format from ibm-granite/granite-3b-code-instruct-128k 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 AIronMind/granite-3b-code-instruct-128k-Q4_K_M-GGUF --hf-file granite-3b-code-instruct-128k-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo AIronMind/granite-3b-code-instruct-128k-Q4_K_M-GGUF --hf-file granite-3b-code-instruct-128k-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 AIronMind/granite-3b-code-instruct-128k-Q4_K_M-GGUF --hf-file granite-3b-code-instruct-128k-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo AIronMind/granite-3b-code-instruct-128k-Q4_K_M-GGUF --hf-file granite-3b-code-instruct-128k-q4_k_m.gguf -c 2048