File size: 3,360 Bytes
3243a70 5aebeaf 3243a70 8bea96e 3243a70 8547527 46979ae 56b9dfe 46979ae 8004d8f 46979ae 527ba6c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 |
---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
license: bigcode-openrail-m
datasets:
- bigcode/the-stack-dedup
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: Tiny-StarCoder-Py
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1
type: pass@1
value: 7.84%
verified: false
---
# TinyStarCoderPy
This is a 164M parameters model with the same architecture as [StarCoder](https://huggingface.co/bigcode/starcoder) (8k context length, MQA & FIM). It was trained on the Python data from [StarCoderData](https://huggingface.co/datasets/bigcode/starcoderdata)
for ~6 epochs which amounts to 100B tokens.
## Use
### Intended use
The model was trained on GitHub code, to assist with some tasks like [Assisted Generation](https://huggingface.co/blog/assisted-generation). For pure code completion, we advise using our 15B models [StarCoder]() or [StarCoderBase]().
### Generation
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "bigcode/tiny_starcoder_py"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
### Fill-in-the-middle
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
```python
input_text = "<fim_prefix>def print_one_two_three():\n print('one')\n <fim_suffix>\n print('three')<fim_middle>"
inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
```
# Training
## Model
- **Architecture:** GPT-2 model with multi-query attention and Fill-in-the-Middle objective
- **Pretraining steps:** 50k
- **Pretraining tokens:** 100 billion
- **Precision:** bfloat16
## Hardware
- **GPUs:** 32 Tesla A100
- **Training time:** 18 hours
## Software
- **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
# License
The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement [here](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_bigcode__tiny_starcoder_py)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 25.43 |
| ARC (25-shot) | 20.99 |
| HellaSwag (10-shot) | 28.77 |
| MMLU (5-shot) | 26.79 |
| TruthfulQA (0-shot) | 47.68 |
| Winogrande (5-shot) | 51.22 |
| GSM8K (5-shot) | 0.99 |
| DROP (3-shot) | 1.57 |
|