--- pipeline_tag: text-generation inference: false license: apache-2.0 datasets: - codeparrot/github-code-clean - bigcode/starcoderdata - open-web-math/open-web-math - math-ai/StackMathQA metrics: - code_eval library_name: transformers tags: - code - granite - llama-cpp - gguf-my-repo base_model: ibm-granite/granite-3b-code-base-128k model-index: - name: granite-3b-code-base-128k results: - task: type: text-generation dataset: name: HumanEvalSynthesis (Python) type: bigcode/humanevalpack metrics: - type: pass@1 value: 36.0 name: pass@1 verified: false - type: pass@1 value: 30.5 name: pass@1 verified: false - type: pass@1 value: 22.4 name: pass@1 verified: false - type: pass@1 value: 19.9 name: pass@1 verified: false - task: type: text-generation dataset: name: RepoQA (Python@16K) type: repoqa metrics: - type: pass@1 (thresh=0.5) value: 40.0 name: pass@1 (thresh=0.5) verified: false - type: pass@1 (thresh=0.5) value: 36.0 name: pass@1 (thresh=0.5) verified: false - type: pass@1 (thresh=0.5) value: 37.0 name: pass@1 (thresh=0.5) verified: false - type: pass@1 (thresh=0.5) value: 27.0 name: pass@1 (thresh=0.5) verified: false - type: pass@1 (thresh=0.5) value: 29.0 name: pass@1 (thresh=0.5) verified: false - task: type: text-generation dataset: name: LCC (Balanced) type: lcc metrics: - type: Exact Match@4K value: 54.6 name: Exact Match@4K verified: false - type: Exact Match@8K value: 56.8 name: Exact Match@8K verified: false - type: Exact Match@16K value: 52.2 name: Exact Match@16K verified: false - type: Exact Match@32K value: 57.8 name: Exact Match@32K verified: false - task: type: text-generation dataset: name: RepoBench-P (Balanced) type: repobench metrics: - type: Exact Match@4K value: 39.8 name: Exact Match@4K verified: false - type: Exact Match@8K value: 46.8 name: Exact Match@8K verified: false - type: Exact Match@16K value: 43.1 name: Exact Match@16K verified: false - type: Exact Match@32K value: 45.3 name: Exact Match@32K verified: false --- # AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF This model was converted to GGUF format from [`ibm-granite/granite-3b-code-base-128k`](https://huggingface.co/ibm-granite/granite-3b-code-base-128k) 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-3b-code-base-128k) 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 AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.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 AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo AIronMind/granite-3b-code-base-128k-Q4_K_M-GGUF --hf-file granite-3b-code-base-128k-q4_k_m.gguf -c 2048 ```