--- base_model: bigcode/starcoder2-15b datasets: - bigcode/the-stack-v2-train library_name: transformers license: bigcode-openrail-m pipeline_tag: text-generation tags: - code - llama-cpp - gguf-my-repo inference: parameters: temperature: 0.2 top_p: 0.95 widget: - text: 'def print_hello_world():' example_title: Hello world group: Python model-index: - name: starcoder2-15b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 48.1 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 33.8 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 65.1 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 37.8 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 46.3 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 74.08 --- # teemperor/starcoder2-15b-Q6_K-GGUF This model was converted to GGUF format from [`bigcode/starcoder2-15b`](https://huggingface.co/bigcode/starcoder2-15b) 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/bigcode/starcoder2-15b) 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 teemperor/starcoder2-15b-Q6_K-GGUF --hf-file starcoder2-15b-q6_k.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo teemperor/starcoder2-15b-Q6_K-GGUF --hf-file starcoder2-15b-q6_k.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 teemperor/starcoder2-15b-Q6_K-GGUF --hf-file starcoder2-15b-q6_k.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo teemperor/starcoder2-15b-Q6_K-GGUF --hf-file starcoder2-15b-q6_k.gguf -c 2048 ```