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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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name: pass@1 |
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- type: pass@1 |
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value: 42.1 |
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name: pass@1 |
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- type: pass@1 |
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value: 45.7 |
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name: pass@1 |
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- type: pass@1 |
|
value: 42.7 |
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name: pass@1 |
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- type: pass@1 |
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value: 44.5 |
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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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name: pass@1 |
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- type: pass@1 |
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value: 49.4 |
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name: pass@1 |
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- type: pass@1 |
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value: 32.3 |
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name: pass@1 |
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- type: pass@1 |
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value: 42.1 |
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name: pass@1 |
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- type: pass@1 |
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value: 18.3 |
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name: pass@1 |
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- type: pass@1 |
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value: 43.9 |
|
name: pass@1 |
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- type: pass@1 |
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value: 43.9 |
|
name: pass@1 |
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- type: pass@1 |
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value: 45.7 |
|
name: pass@1 |
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- type: pass@1 |
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value: 41.5 |
|
name: pass@1 |
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- type: pass@1 |
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value: 41.5 |
|
name: pass@1 |
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- type: pass@1 |
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value: 29.9 |
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name: pass@1 |
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--- |
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|
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# cobrakenji/granite-20b-code-instruct-Q5_K_M-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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|
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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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|
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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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|
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### CLI: |
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```bash |
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llama --hf-repo cobrakenji/granite-20b-code-instruct-Q5_K_M-GGUF --hf-file granite-20b-code-instruct-q5_k_m.gguf -p "The meaning to life and the universe is" |
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``` |
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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-Q5_K_M-GGUF --hf-file granite-20b-code-instruct-q5_k_m.gguf -c 2048 |
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``` |
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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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|
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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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|
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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-Q5_K_M-GGUF --hf-file granite-20b-code-instruct-q5_k_m.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-Q5_K_M-GGUF --hf-file granite-20b-code-instruct-q5_k_m.gguf -c 2048 |
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``` |
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