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README.md
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---
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license: apache-2.0
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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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- llama-cpp
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- gguf-my-repo
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base_model: ibm-granite/granite-20b-code-instruct
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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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pipeline_tag: text-generation
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inference: true
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model-index:
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- name: granite-20b-code-instruct
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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: 60.4
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name: pass@1
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- type: pass@1
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value: 53.7
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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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| 40 |
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name: pass@1
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| 41 |
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- type: pass@1
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| 42 |
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value: 42.1
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| 43 |
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name: pass@1
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| 44 |
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- type: pass@1
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| 45 |
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value: 45.7
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| 46 |
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name: pass@1
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| 47 |
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- type: pass@1
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| 48 |
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value: 42.7
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| 49 |
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name: pass@1
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| 50 |
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- type: pass@1
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| 51 |
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value: 44.5
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| 52 |
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name: pass@1
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| 53 |
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- type: pass@1
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| 54 |
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value: 42.7
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| 55 |
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name: pass@1
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| 56 |
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- type: pass@1
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| 57 |
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value: 49.4
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| 58 |
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name: pass@1
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| 59 |
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- type: pass@1
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| 60 |
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value: 32.3
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| 61 |
+
name: pass@1
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| 62 |
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- type: pass@1
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| 63 |
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value: 42.1
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| 64 |
+
name: pass@1
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| 65 |
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- type: pass@1
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| 66 |
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value: 18.3
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| 67 |
+
name: pass@1
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| 68 |
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- type: pass@1
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| 69 |
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value: 43.9
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| 70 |
+
name: pass@1
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| 71 |
+
- type: pass@1
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| 72 |
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value: 43.9
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| 73 |
+
name: pass@1
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| 74 |
+
- type: pass@1
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| 75 |
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value: 45.7
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| 76 |
+
name: pass@1
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| 77 |
+
- type: pass@1
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| 78 |
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value: 41.5
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| 79 |
+
name: pass@1
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| 80 |
+
- type: pass@1
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| 81 |
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value: 41.5
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| 82 |
+
name: pass@1
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| 83 |
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- type: pass@1
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| 84 |
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value: 29.9
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| 85 |
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name: pass@1
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| 86 |
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---
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# cobrakenji/granite-20b-code-instruct-Q2_K-GGUF
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This model was converted to GGUF format from [`ibm-granite/granite-20b-code-instruct`](https://huggingface.co/ibm-granite/granite-20b-code-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
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Refer to the [original model card](https://huggingface.co/ibm-granite/granite-20b-code-instruct) for more details on the model.
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## Use with llama.cpp
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Install llama.cpp through brew (works on Mac and Linux)
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```bash
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brew install llama.cpp
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```
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Invoke the llama.cpp server or the CLI.
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### CLI:
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```bash
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llama --hf-repo cobrakenji/granite-20b-code-instruct-Q2_K-GGUF --hf-file granite-20b-code-instruct-q2_k.gguf -p "The meaning to life and the universe is"
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```
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### Server:
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```bash
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llama-server --hf-repo cobrakenji/granite-20b-code-instruct-Q2_K-GGUF --hf-file granite-20b-code-instruct-q2_k.gguf -c 2048
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```
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Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
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Step 1: Clone llama.cpp from GitHub.
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```
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git clone https://github.com/ggerganov/llama.cpp
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```
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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).
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```
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cd llama.cpp && LLAMA_CURL=1 make
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```
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Step 3: Run inference through the main binary.
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```
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./main --hf-repo cobrakenji/granite-20b-code-instruct-Q2_K-GGUF --hf-file granite-20b-code-instruct-q2_k.gguf -p "The meaning to life and the universe is"
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```
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or
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```
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./server --hf-repo cobrakenji/granite-20b-code-instruct-Q2_K-GGUF --hf-file granite-20b-code-instruct-q2_k.gguf -c 2048
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```
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