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
💫 Community Model> Starcoder2 15B Instruct v0.1 by BigCode
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Model creator: bigcode
Original model: starcoder2-15b-instruct-v0.1
GGUF quantization: provided by bartowski based on llama.cpp release b2756
We introduce StarCoder2-15B-Instruct-v0.1, the very first entirely self-aligned code Large Language Model (LLM) trained with a fully permissive and transparent pipeline. Our open-source pipeline uses StarCoder2-15B to generate thousands of instruction-response pairs, which are then used to fine-tune StarCoder-15B itself without any human annotations or distilled data from huge and proprietary LLMs.
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
Creating unit tests
More coding examples
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
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 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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