StarCoder2: The Open Coding Model Trained on 600+ Programming Languages

What Is StarCoder2?

StarCoder2 is the second generation of the BigCode project's open coding models, released in February 2024. The project is a collaboration between Hugging Face, ServiceNow, and the broader open-source research community. The flagship 15B model is trained on The Stack v2 - a cleaned and deduplicated dataset of source code across 619 programming languages.

Model Variants

BigCode released three sizes:

• StarCoder2-3B - fits on a single consumer GPU (12GB VRAM), good for autocomplete
• StarCoder2-7B - balanced size for most IDE integration use cases
• StarCoder2-15B - best quality, recommended for generation tasks

All three are released under the BigCode Open RAIL-M license, which allows commercial use with attribution.

HumanEval and Benchmark Comparisons

The instruct-tuned version (StarCoder2-15B-Instruct-v0.1) reaches competitive scores on instruction following, though newer models like Qwen2.5-Coder have surpassed it on raw HumanEval. StarCoder2 remains relevant for its breadth of language coverage and fill-in-the-middle support.

Fill-in-the-Middle Training

StarCoder2 is trained with the FIM (fill-in-the-middle) objective, using a special set of tokens that makes code completion feel natural:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("bigcode/starcoder2-15b")
model = AutoModelForCausalLM.from_pretrained(
    "bigcode/starcoder2-15b",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prefix = "def is_palindrome(s: str) -> bool:\n    "
suffix = "\n\nassert is_palindrome('racecar') == True"

fim_prompt = f"<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>"

inputs = tokenizer(fim_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

VS Code Integration via Continue

The easiest way to use StarCoder2-15B for day-to-day coding is through the Continue extension:

1. Install Continue from the VS Code marketplace
2. In ~/.continue/config.json, add:

{
  "tabAutocompleteModel": {
    "title": "StarCoder2",
    "provider": "ollama",
    "model": "starcoder2:15b"
  }
}

1. Run ollama pull starcoder2:15b

You now have local, private tab completion that never sends your code to a third-party server.

Hugging Face Code Leaderboard

The Big Code Models Leaderboard tracks models on HumanEval, MBPP, MultiPL-E, and DS-1000. As of early 2025, StarCoder2-15B sits in the top 10 for models under 20B parameters, though the Qwen2.5-Coder family has moved above it in absolute scores.

When to Choose StarCoder2

• You need maximum language breadth (619 languages vs ~100 for most models)
• You want IDE tab completion with FIM and full local privacy
• You need a model in the 7 - 15B range that balances quality and hardware requirements
• You want to build on top of a permissively licensed, academically documented model

Links

• BigCode project
• HuggingFace model page
• Technical paper