starcoder2-3b
StarCoder2-3B model is a 3B parameter model trained on 17 programming languages from The Stack v2, with opt-out requests excluded. The model uses Grouped Query Attention, a context window of 16,384 tokens with a sliding window attention of 4,096 tokens, and was trained using the Fill-in-the-Middle objective on 3+ trillion tokens.
Params
3.0 B
Context
16,384
Downloads 30d
184 K
Likes
221
Download history
daily snapshots · 10 days200 K183 K
Jul 28Jul 31Aug 3Aug 6
Can you run it?
Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.
| File | Quant | Size | Est. VRAM | Verdict on RTX 4090 · 24 GB |
|---|---|---|---|---|
| model.safetensors | f32 | 12.1 GB | 14.3 GB | ✅ Runs comfortably |
| model.safetensors (bf16, full) | bf16 + 16K ctx | 12.1 GB | 14.7 GB | ✅ Runs comfortably |
Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.
Run it
copy-paste, exact tags checked against the Hub$ curl -s https://aimodelscomparison.com/api/v1/models/starcoder2-3b
{
"hf_id": "bigcode/starcoder2-3b",
"params_b": 3.03,
"context_length": 16384,
"license": { "id": "bigcode-openrail-m", "commercial": "unknown" },
"downloads_30d": 183925,
"vram_estimates": [
{ "quant": "f32", "gb": 14.3 }
],
"updated_at": "2026-07-28T18:08:09Z"
}
Specifications
- Architecture
- Starcoder2ForCausalLM
- Parameters
- 3.0 B
- Tensor type
- F32
- Context length
- 16,384
- Vocabulary
- 49,152
- Layers / heads
- 30 / 24
- Licence
- bigcode-openrail-m
- First seen on the Hub
- 2023-11-29
- Training datasets
- bigcode/the-stack-v2-train
- DS-1000 (reported)
- 25
- HumanEval (reported)
- 31.7
- CruxEval-I (reported)
- 32.7
- HumanEval+ (reported)
- 27.4
- GSM8K (PAL) (reported)
- 27.7
- RepoBench-v1.1 (reported)
- 71.19
- Added to our catalog
- 2026-07-28
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