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metadata
pipeline_tag: text-generation
base_model: bigcode/starcoder2-15b
datasets:
  - bigcode/self-oss-instruct-sc2-exec-filter-50k
license: bigcode-openrail-m
library_name: transformers
tags:
  - code
model-index:
  - name: starcoder2-15b-instruct-v0.1
    results:
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (code generation)
          type: livecodebench-codegeneration
        metrics:
          - type: pass@1
            value: 20.4
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (self repair)
          type: livecodebench-selfrepair
        metrics:
          - type: pass@1
            value: 20.9
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (test output prediction)
          type: livecodebench-testoutputprediction
        metrics:
          - type: pass@1
            value: 29.8
      - task:
          type: text-generation
        dataset:
          name: LiveCodeBench (code execution)
          type: livecodebench-codeexecution
        metrics:
          - type: pass@1
            value: 28.1
      - task:
          type: text-generation
        dataset:
          name: HumanEval
          type: humaneval
        metrics:
          - type: pass@1
            value: 72.6
      - task:
          type: text-generation
        dataset:
          name: HumanEval+
          type: humanevalplus
        metrics:
          - type: pass@1
            value: 63.4
      - task:
          type: text-generation
        dataset:
          name: MBPP
          type: mbpp
        metrics:
          - type: pass@1
            value: 75.2
      - task:
          type: text-generation
        dataset:
          name: MBPP+
          type: mbppplus
        metrics:
          - type: pass@1
            value: 61.2
      - task:
          type: text-generation
        dataset:
          name: DS-1000
          type: ds-1000
        metrics:
          - type: pass@1
            value: 40.6
quantized_by: bartowski
lm_studio:
  param_count: 15b
  use_case: coding
  release_date: 30-04-2024
  model_creator: BigCode
  prompt_template: Starcoder2 Instruct
  system_prompt: none
  base_model: starcoder2
  original_repo: bigcode/starcoder2-15b-instruct-v0.1

πŸ’« Community Model> Starcoder2 15B Instruct v0.1 by BigCode

πŸ‘Ύ LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord.

Model creator: bigcode
Original model: starcoder2-15b-instruct-v0.1
GGUF quantization: provided by bartowski based on llama.cpp release b2756

Model Summary:

Starcoder2-15B-Instruct-v0.1 is self-proclaimed to be the first entirely self-aligned code model with a fully permissive and transparent pipeline.
This model is meant to be used for coding instructions in a single turn, any other styles may result in less accurate responses.
Starcoder2 has been primarily finetuned for Python code generation and as such should primarily be used for Python tasks.

Prompt Template:

Choose the 'Starcoder2 Instruct' preset in your LM Studio.

Under the hood, the model will see a prompt that's formatted like so:

<|endoftext|>You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.

### Instruction
{prompt}

### Response
<|endoftext|>

Use case and examples

This model should be used for single turn coding related instructions.

Coding with requirements

image/png

Creating unit tests

image/png

More coding examples

image/png

Technical Details

Starcoder2 15B instruct was trained primarily on Python code generation tasks. Using Starcoder2 15B (non instruct) to generated thousands of instruction-reponse pairs, the results were used to fine tune an instruct model without human annotation or distilled data.

The dataset created is open and available: self-oss-instruct-sc2-exec-filter-50k

And the code used to create the self-alignment has been shared here: starcoder2-self-align

The results of the self-alignment are extremely promising, with significantly higher scores across all coding benchmarks, which is a great sign for future progress.

More details on their model card here

Special thanks

πŸ™ Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

πŸ™ Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for these quants, which improves the overall quality!

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