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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
  - TensorBlock
  - GGUF
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
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ibm-granite/granite-3b-code-instruct-128k - GGUF

This repo contains GGUF format model files for ibm-granite/granite-3b-code-instruct-128k.

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

Prompt template 

System:
{system_prompt}

Question:
{prompt}

Answer:

Model file specification

Filename Quant type File Size Description
granite-3b-code-instruct-128k-Q2_K.gguf Q2_K 1.247 GB smallest, significant quality loss - not recommended for most purposes
granite-3b-code-instruct-128k-Q3_K_S.gguf Q3_K_S 1.445 GB very small, high quality loss
granite-3b-code-instruct-128k-Q3_K_M.gguf Q3_K_M 1.608 GB very small, high quality loss
granite-3b-code-instruct-128k-Q3_K_L.gguf Q3_K_L 1.747 GB small, substantial quality loss
granite-3b-code-instruct-128k-Q4_0.gguf Q4_0 1.860 GB legacy; small, very high quality loss - prefer using Q3_K_M
granite-3b-code-instruct-128k-Q4_K_S.gguf Q4_K_S 1.875 GB small, greater quality loss
granite-3b-code-instruct-128k-Q4_K_M.gguf Q4_K_M 1.986 GB medium, balanced quality - recommended
granite-3b-code-instruct-128k-Q5_0.gguf Q5_0 2.251 GB legacy; medium, balanced quality - prefer using Q4_K_M
granite-3b-code-instruct-128k-Q5_K_S.gguf Q5_K_S 2.251 GB large, low quality loss - recommended
granite-3b-code-instruct-128k-Q5_K_M.gguf Q5_K_M 2.316 GB large, very low quality loss - recommended
granite-3b-code-instruct-128k-Q6_K.gguf Q6_K 2.666 GB very large, extremely low quality loss
granite-3b-code-instruct-128k-Q8_0.gguf Q8_0 3.451 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-3b-code-instruct-128k-GGUF --include "granite-3b-code-instruct-128k-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-3b-code-instruct-128k-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'