StarCoder2-Instruct-GGUF
- This is quantized version of bigcode/starcoder2-15b-instruct-v0.1 created using llama.cpp
Model Summary
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: bigcode/starcoder2-15b-instruct-v0.1
- Code: bigcode-project/starcoder2-self-align
- Dataset: bigcode/self-oss-instruct-sc2-exec-filter-50k
- Authors: Yuxiang Wei, Federico Cassano, Jiawei Liu, Yifeng Ding, Naman Jain, Harm de Vries, Leandro von Werra, Arjun Guha, Lingming Zhang.
Use
Intended use
The model is designed to respond to coding-related instructions in a single turn. Instructions in other styles may result in less accurate responses.
Bias, Risks, and Limitations
StarCoder2-15B-Instruct-v0.1 is primarily finetuned for Python code generation tasks that can be verified through execution, which may lead to certain biases and limitations. For example, the model might not adhere strictly to instructions that dictate the output format. In these situations, it's beneficial to provide a response prefix or a one-shot example to steer the model’s output. Additionally, the model may have limitations with other programming languages and out-of-domain coding tasks.
The model also inherits the bias, risks, and limitations from its base StarCoder2-15B model. For more information, please refer to the StarCoder2-15B model card.
Evaluation on EvalPlus, LiveCodeBench, and DS-1000
Training Details
Hyperparameters
- Optimizer: Adafactor
- Learning rate: 1e-5
- Epoch: 4
- Batch size: 64
- Warmup ratio: 0.05
- Scheduler: Linear
- Sequence length: 1280
- Dropout: Not applied
Hardware
1 x NVIDIA A100 80GB