tiny_starcoder_py
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.
Params
160 M
Context
—
Downloads 30d
249 K
Likes
74
Download history
daily snapshots · 10 days257 K246 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 | 0.7 GB | 1.2 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/tiny-starcoder-py
{
"hf_id": "bigcode/tiny_starcoder_py",
"params_b": 0.16,
"context_length": null,
"license": { "id": "bigcode-openrail-m", "commercial": "unknown" },
"downloads_30d": 249481,
"vram_estimates": [
{ "quant": "f32", "gb": 1.2 }
],
"updated_at": "2026-07-28T18:07:14Z"
}
Specifications
- Architecture
- GPTBigCodeForCausalLM
- Parameters
- 160 M
- Tensor type
- F32
- Vocabulary
- 49,152
- Licence
- bigcode-openrail-m
- First seen on the Hub
- 2023-05-15
- Training datasets
- bigcode/the-stack-dedup
- Added to our catalog
- 2026-07-28
Compare with
Sponsored · GPU cloud
Not enough VRAM?
Spin up a 24 GB L4 instance in 40 seconds. $0.44/hr.