--- pipeline_tag: text-generation inference: true widget: - text: 'def print_hello_world():' example_title: Hello world group: Python datasets: - bigcode/the-stack-v2-train license: bigcode-openrail-m library_name: transformers tags: - code - TensorBlock - GGUF base_model: bigcode/starcoder2-3b model-index: - name: starcoder2-3b results: - task: type: text-generation dataset: name: CruxEval-I type: cruxeval-i metrics: - type: pass@1 value: 32.7 - task: type: text-generation dataset: name: DS-1000 type: ds-1000 metrics: - type: pass@1 value: 25.0 - task: type: text-generation dataset: name: GSM8K (PAL) type: gsm8k-pal metrics: - type: accuracy value: 27.7 - task: type: text-generation dataset: name: HumanEval+ type: humanevalplus metrics: - type: pass@1 value: 27.4 - task: type: text-generation dataset: name: HumanEval type: humaneval metrics: - type: pass@1 value: 31.7 - task: type: text-generation dataset: name: RepoBench-v1.1 type: repobench-v1.1 metrics: - type: edit-smiliarity value: 71.19 ---
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## bigcode/starcoder2-3b - GGUF This repo contains GGUF format model files for [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b). The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d). ## Our projects
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## Prompt template ``` ``` ## Model file specification | Filename | Quant type | File Size | Description | | -------- | ---------- | --------- | ----------- | | [starcoder2-3b-Q2_K.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q2_K.gguf) | Q2_K | 1.139 GB | smallest, significant quality loss - not recommended for most purposes | | [starcoder2-3b-Q3_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q3_K_S.gguf) | Q3_K_S | 1.273 GB | very small, high quality loss | | [starcoder2-3b-Q3_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q3_K_M.gguf) | Q3_K_M | 1.455 GB | very small, high quality loss | | [starcoder2-3b-Q3_K_L.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q3_K_L.gguf) | Q3_K_L | 1.618 GB | small, substantial quality loss | | [starcoder2-3b-Q4_0.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q4_0.gguf) | Q4_0 | 1.629 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [starcoder2-3b-Q4_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q4_K_S.gguf) | Q4_K_S | 1.642 GB | small, greater quality loss | | [starcoder2-3b-Q4_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q4_K_M.gguf) | Q4_K_M | 1.758 GB | medium, balanced quality - recommended | | [starcoder2-3b-Q5_0.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q5_0.gguf) | Q5_0 | 1.964 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [starcoder2-3b-Q5_K_S.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q5_K_S.gguf) | Q5_K_S | 1.964 GB | large, low quality loss - recommended | | [starcoder2-3b-Q5_K_M.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q5_K_M.gguf) | Q5_K_M | 2.031 GB | large, very low quality loss - recommended | | [starcoder2-3b-Q6_K.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q6_K.gguf) | Q6_K | 2.320 GB | very large, extremely low quality loss | | [starcoder2-3b-Q8_0.gguf](https://huggingface.co/tensorblock/starcoder2-3b-GGUF/blob/main/starcoder2-3b-Q8_0.gguf) | Q8_0 | 3.003 GB | very large, extremely low quality loss - not recommended | ## Downloading instruction ### Command line Firstly, install Huggingface Client ```shell pip install -U "huggingface_hub[cli]" ``` Then, downoad the individual model file the a local directory ```shell huggingface-cli download tensorblock/starcoder2-3b-GGUF --include "starcoder2-3b-Q2_K.gguf" --local-dir MY_LOCAL_DIR ``` If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try: ```shell huggingface-cli download tensorblock/starcoder2-3b-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf' ```