Turkish GPT-2 Large
GPT-2 Large architecture trained from scratch on Turkish. Reference baseline for measuring how much modern instruction-tuned models actually improve on the GPT-2 era.
Overview
GPT-2 Large architecture trained from scratch on Turkish. Reference baseline for measuring how much modern instruction-tuned models actually improve on the GPT-2 era.
Strengths
- Tiny footprint — runs anywhere
- Useful as a reference baseline when evaluating Turkish-tuned modern models
- MIT license; total freedom for any use
Weaknesses
- GPT-2 era model — quality far below any 2024+ Turkish fine-tune
- 1024 context, completion-only (no chat template)
- Best treated as a research/historical reference, not a production option
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 0.4 GB | 1 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Turkish GPT-2 Large.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Turkish GPT-2 Large?
Can I use Turkish GPT-2 Large commercially?
What's the context length of Turkish GPT-2 Large?
Source: huggingface.co/ytu-ce-cosmos/turkish-gpt2-large
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Turkish GPT-2 Large runs on your specific hardware before committing money.