metadata
license: apache-2.0
library_name: transformers
tags:
- code
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-20b-code-base
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
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-20b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 60.4
name: pass@1
- type: pass@1
value: 53.7
name: pass@1
- type: pass@1
value: 58.5
name: pass@1
- type: pass@1
value: 42.1
name: pass@1
- type: pass@1
value: 45.7
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 44.5
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 49.4
name: pass@1
- type: pass@1
value: 32.3
name: pass@1
- type: pass@1
value: 42.1
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 45.7
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 29.9
name: pass@1
NikolayKozloff/granite-20b-code-instruct-Q4_0-GGUF
This model was converted to GGUF format from ibm-granite/granite-20b-code-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.
brew install ggerganov/ggerganov/llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo NikolayKozloff/granite-20b-code-instruct-Q4_0-GGUF --model granite-20b-code-instruct.Q4_0.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo NikolayKozloff/granite-20b-code-instruct-Q4_0-GGUF --model granite-20b-code-instruct.Q4_0.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m granite-20b-code-instruct.Q4_0.gguf -n 128