Triangle104
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---
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pipeline_tag: text-generation
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inference: false
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license: apache-2.0
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library_name: transformers
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tags:
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- language
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- granite-3.0
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- llama-cpp
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- gguf-my-repo
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base_model: ibm-granite/granite-3.0-8b-instruct
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model-index:
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- name: granite-3.0-2b-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: IFEval
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type: instruction-following
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metrics:
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- type: pass@1
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value: 52.27
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name: pass@1
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- type: pass@1
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value: 8.22
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: AGI-Eval
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type: human-exams
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metrics:
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- type: pass@1
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value: 40.52
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name: pass@1
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- type: pass@1
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value: 65.82
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name: pass@1
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- type: pass@1
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value: 34.45
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: OBQA
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type: commonsense
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metrics:
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- type: pass@1
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value: 46.6
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name: pass@1
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- type: pass@1
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value: 71.21
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name: pass@1
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- type: pass@1
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value: 82.61
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name: pass@1
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- type: pass@1
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value: 77.51
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name: pass@1
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- type: pass@1
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value: 60.32
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: BoolQ
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type: reading-comprehension
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metrics:
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- type: pass@1
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value: 88.65
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name: pass@1
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- type: pass@1
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value: 21.58
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: ARC-C
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type: reasoning
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metrics:
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- type: pass@1
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value: 64.16
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name: pass@1
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- type: pass@1
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value: 33.81
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name: pass@1
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- type: pass@1
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value: 51.55
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: HumanEvalSynthesis
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type: code
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metrics:
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- type: pass@1
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value: 64.63
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name: pass@1
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- type: pass@1
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value: 57.16
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name: pass@1
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- type: pass@1
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value: 65.85
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name: pass@1
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- type: pass@1
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value: 49.6
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: GSM8K
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type: math
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metrics:
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- type: pass@1
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value: 68.99
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name: pass@1
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- type: pass@1
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value: 30.94
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name: pass@1
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- task:
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type: text-generation
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dataset:
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name: PAWS-X (7 langs)
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type: multilingual
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metrics:
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- type: pass@1
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value: 64.94
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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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---
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# Triangle104/granite-3.0-8b-instruct-Q4_K_S-GGUF
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This model was converted to GGUF format from [`ibm-granite/granite-3.0-8b-instruct`](https://huggingface.co/ibm-granite/granite-3.0-8b-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-3.0-8b-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-cli --hf-repo Triangle104/granite-3.0-8b-instruct-Q4_K_S-GGUF --hf-file granite-3.0-8b-instruct-q4_k_s.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 Triangle104/granite-3.0-8b-instruct-Q4_K_S-GGUF --hf-file granite-3.0-8b-instruct-q4_k_s.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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./llama-cli --hf-repo Triangle104/granite-3.0-8b-instruct-Q4_K_S-GGUF --hf-file granite-3.0-8b-instruct-q4_k_s.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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./llama-server --hf-repo Triangle104/granite-3.0-8b-instruct-Q4_K_S-GGUF --hf-file granite-3.0-8b-instruct-q4_k_s.gguf -c 2048
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```
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