What are the hardware requirements for Tiny Starcoder Py?

Tiny Starcoder Py requires approximately 0.7 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Tiny Starcoder Py and how large is it?

Tiny Starcoder Py is developed by bigcode, a model with 164.1m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Tiny Starcoder Py perform on standard benchmarks?

Tiny Starcoder Py has the following published scores: MMLU 26.79. Compare against reference models on this page or on the LLM Explorer leaderboards.

Tiny Starcoder Py by bigcode — VRAM 0.7GB

Name: Tiny Starcoder Py
Author: bigcode

Tiny Starcoder Py is an open-source language model by bigcode. Features: 164.1m LLM, VRAM: 0.7GB, License: bigcode-openrail-m, Code Generating, LLM Explorer Score: 0.19, Arc: 21, HellaSwag: 28.8, MMLU: 26.8, GSM8K: 1.

Code Codegen Dataset:bigcode/the-stack-dedu... Endpoints compatible Gpt bigcode Model-index Pytorch Region:us Safetensors

Model Card on HF 🤗: https://huggingface.co/bigcode/tiny_starcoder_py

Tiny Starcoder Py Parameters and Internals

Model Type

text generation

Use Cases

Primary Use Cases:

Assisted Generation

Training Details

Data Sources:

StarCoderData

Data Volume:

100 billion tokens

Context Length:

8000

Training Time:

18 hours

Hardware Used:

32 Tesla A100 GPUs

Model Architecture:

GPT-2 model with multi-query attention and Fill-in-the-Middle objective

Input Output

Input Format:

text (Python code)

Output Format:

generated code

LLM Name	Tiny Starcoder Py
Repository 🤗	https://huggingface.co/bigcode/tiny_starcoder_py
Model Size	164.1m
Required VRAM	0.7 GB
Updated	2026-07-29
Maintainer	bigcode
Model Type	gpt_bigcode
Model Files	0.7 GB 0.7 GB
Generates Code	Yes
Model Architecture	GPTBigCodeForCausalLM
License	bigcode-openrail-m
Transformers Version	4.28.1
Tokenizer Class	GPT2Tokenizer
Vocabulary Size	49152
Torch Data Type	float32
Activation Function	gelu_pytorch_tanh

Rank the Tiny Starcoder Py capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

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