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tiny_starcoder_py - GGUF

• Model creator: https://huggingface.co/bigcode/
• Original model: https://huggingface.co/bigcode/tiny_starcoder_py/

Name	Quant method	Size
tiny_starcoder_py.Q2_K.gguf	Q2_K	0.1GB
tiny_starcoder_py.IQ3_XS.gguf	IQ3_XS	0.1GB
tiny_starcoder_py.IQ3_S.gguf	IQ3_S	0.1GB
tiny_starcoder_py.Q3_K_S.gguf	Q3_K_S	0.1GB
tiny_starcoder_py.IQ3_M.gguf	IQ3_M	0.11GB
tiny_starcoder_py.Q3_K.gguf	Q3_K	0.11GB
tiny_starcoder_py.Q3_K_M.gguf	Q3_K_M	0.11GB
tiny_starcoder_py.Q3_K_L.gguf	Q3_K_L	0.12GB
tiny_starcoder_py.IQ4_XS.gguf	IQ4_XS	0.11GB
tiny_starcoder_py.Q4_0.gguf	Q4_0	0.12GB
tiny_starcoder_py.IQ4_NL.gguf	IQ4_NL	0.12GB
tiny_starcoder_py.Q4_K_S.gguf	Q4_K_S	0.12GB
tiny_starcoder_py.Q4_K.gguf	Q4_K	0.12GB
tiny_starcoder_py.Q4_K_M.gguf	Q4_K_M	0.12GB
tiny_starcoder_py.Q4_1.gguf	Q4_1	0.12GB
tiny_starcoder_py.Q5_0.gguf	Q5_0	0.13GB
tiny_starcoder_py.Q5_K_S.gguf	Q5_K_S	0.13GB
tiny_starcoder_py.Q5_K.gguf	Q5_K	0.14GB
tiny_starcoder_py.Q5_K_M.gguf	Q5_K_M	0.14GB
tiny_starcoder_py.Q5_1.gguf	Q5_1	0.14GB
tiny_starcoder_py.Q6_K.gguf	Q6_K	0.15GB
tiny_starcoder_py.Q8_0.gguf	Q8_0	0.18GB

Original model description:

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 (8k context length, MQA & FIM). It was trained on the Python data from 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. For pure code completion, we advise using our 15B models StarCoder or StarCoderBase.

Generation

# 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:

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
• Neural networks: PyTorch
• BP16 if applicable: apex

License

The model is licensed under the BigCode OpenRAIL-M v1 license agreement. You can find the full agreement here.