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