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README.md
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| 1 |
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---
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| 2 |
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pipeline_tag: text-generation
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base_model: ibm-granite/granite-8b-code-instruct-4k
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inference: false
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license: apache-2.0
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datasets:
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- bigcode/commitpackft
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- TIGER-Lab/MathInstruct
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- meta-math/MetaMathQA
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- glaiveai/glaive-code-assistant-v3
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- glaive-function-calling-v2
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- bugdaryan/sql-create-context-instruction
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- garage-bAInd/Open-Platypus
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- nvidia/HelpSteer
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metrics:
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- code_eval
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library_name: transformers
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tags:
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- code
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- granite
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- openvino
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- openvino-export
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model-index:
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- name: granite-8b-code-instruct-4k
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results:
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- task:
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type: text-generation
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dataset:
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name: HumanEvalSynthesis(Python)
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type: bigcode/humanevalpack
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metrics:
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- type: pass@1
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value: 57.9
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name: pass@1
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- type: pass@1
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value: 52.4
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name: pass@1
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- type: pass@1
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value: 58.5
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name: pass@1
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- type: pass@1
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value: 43.3
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name: pass@1
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- type: pass@1
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value: 48.2
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name: pass@1
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- type: pass@1
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value: 37.2
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name: pass@1
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- type: pass@1
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value: 53.0
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name: pass@1
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- type: pass@1
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value: 42.7
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| 55 |
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name: pass@1
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- type: pass@1
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value: 52.4
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| 58 |
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name: pass@1
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- type: pass@1
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value: 36.6
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name: pass@1
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- type: pass@1
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value: 43.9
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| 64 |
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name: pass@1
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- type: pass@1
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value: 16.5
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| 67 |
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name: pass@1
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| 68 |
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- type: pass@1
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value: 39.6
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| 70 |
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name: pass@1
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| 71 |
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- type: pass@1
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value: 40.9
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| 73 |
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name: pass@1
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| 74 |
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- type: pass@1
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value: 48.2
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| 76 |
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name: pass@1
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| 77 |
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- type: pass@1
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| 78 |
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value: 41.5
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| 79 |
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name: pass@1
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| 80 |
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- type: pass@1
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| 81 |
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value: 39.0
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| 82 |
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name: pass@1
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- type: pass@1
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value: 32.9
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name: pass@1
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---
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This model was converted to OpenVINO from [`ibm-granite/granite-8b-code-instruct-4k`](https://huggingface.co/ibm-granite/granite-8b-code-instruct-4k) using [optimum-intel](https://github.com/huggingface/optimum-intel)
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via the [export](https://huggingface.co/spaces/echarlaix/openvino-export) space.
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First make sure you have optimum-intel installed:
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```bash
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pip install optimum[openvino]
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```
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To load your model you can do as follows:
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```python
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from optimum.intel import OVModelForCausalLM
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model_id = "NitroLLM/granite-8b-code-instruct-4k-openvino"
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model = OVModelForCausalLM.from_pretrained(model_id)
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```
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