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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-3.0-3b-a800m-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: 42.49
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name: pass@1
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- type: pass@1
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value: 7.02
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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: 25.7
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name: pass@1
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- type: pass@1
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value: 50.16
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+
name: pass@1
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- type: pass@1
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value: 20.51
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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: 40.8
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name: pass@1
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- type: pass@1
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value: 59.95
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+
name: pass@1
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- type: pass@1
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value: 71.86
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+
name: pass@1
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- type: pass@1
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value: 67.01
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+
name: pass@1
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- type: pass@1
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value: 48.0
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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: 78.65
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+
name: pass@1
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+
- type: pass@1
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value: 6.71
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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: 50.94
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name: pass@1
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- type: pass@1
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value: 26.85
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name: pass@1
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- type: pass@1
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value: 37.7
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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: 39.63
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+
name: pass@1
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- type: pass@1
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value: 40.85
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+
name: pass@1
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- type: pass@1
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value: 35.98
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+
name: pass@1
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- type: pass@1
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value: 27.4
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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: 47.54
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name: pass@1
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- type: pass@1
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value: 19.86
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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: 50.23
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name: pass@1
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- type: pass@1
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value: 28.87
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name: pass@1
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---
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# danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF
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This model was converted to GGUF format from [`ibm-granite/granite-3.0-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-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-3.0-3b-a800m-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 danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF --hf-file granite-3.0-3b-a800m-instruct-q8_0.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 danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF --hf-file granite-3.0-3b-a800m-instruct-q8_0.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 danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF --hf-file granite-3.0-3b-a800m-instruct-q8_0.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 danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF --hf-file granite-3.0-3b-a800m-instruct-q8_0.gguf -c 2048
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```
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