--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - granite-3.0 - llama-cpp - gguf-my-repo base_model: ibm-granite/granite-3.0-3b-a800m-instruct model-index: - name: granite-3.0-2b-instruct results: - task: type: text-generation dataset: name: IFEval type: instruction-following metrics: - type: pass@1 value: 42.49 name: pass@1 - type: pass@1 value: 7.02 name: pass@1 - task: type: text-generation dataset: name: AGI-Eval type: human-exams metrics: - type: pass@1 value: 25.7 name: pass@1 - type: pass@1 value: 50.16 name: pass@1 - type: pass@1 value: 20.51 name: pass@1 - task: type: text-generation dataset: name: OBQA type: commonsense metrics: - type: pass@1 value: 40.8 name: pass@1 - type: pass@1 value: 59.95 name: pass@1 - type: pass@1 value: 71.86 name: pass@1 - type: pass@1 value: 67.01 name: pass@1 - type: pass@1 value: 48.0 name: pass@1 - task: type: text-generation dataset: name: BoolQ type: reading-comprehension metrics: - type: pass@1 value: 78.65 name: pass@1 - type: pass@1 value: 6.71 name: pass@1 - task: type: text-generation dataset: name: ARC-C type: reasoning metrics: - type: pass@1 value: 50.94 name: pass@1 - type: pass@1 value: 26.85 name: pass@1 - type: pass@1 value: 37.7 name: pass@1 - task: type: text-generation dataset: name: HumanEvalSynthesis type: code metrics: - type: pass@1 value: 39.63 name: pass@1 - type: pass@1 value: 40.85 name: pass@1 - type: pass@1 value: 35.98 name: pass@1 - type: pass@1 value: 27.4 name: pass@1 - task: type: text-generation dataset: name: GSM8K type: math metrics: - type: pass@1 value: 47.54 name: pass@1 - type: pass@1 value: 19.86 name: pass@1 - task: type: text-generation dataset: name: PAWS-X (7 langs) type: multilingual metrics: - type: pass@1 value: 50.23 name: pass@1 - type: pass@1 value: 28.87 name: pass@1 --- # danqingximeng/granite-3.0-3b-a800m-instruct-Q8_0-GGUF 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. Refer to the [original model card](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash 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" ``` ### Server: ```bash 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 ``` 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. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` 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). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./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" ``` or ``` ./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 ```