GPT-2 Spanish
GPT-2 Spanish is a 124M-parameter model trained from scratch on 11.5GB of Spanish text (Wikipedia, books) with a custom Spanish BPE tokenizer. It generates Spanish prose but is not instruction-tuned — it completes text, it does not follow prompts. Context window is 1024 tokens.
This model made sense in 2020; in 2024 it is hard to recommend for production use. If you need a tiny Spanish base model for fine-tuning experiments or educational work, it is a functional and freely licensed starting point. For anything user-facing, modern 1B+ instruction-tuned Spanish models give dramatically better results at still-modest hardware cost. Skip unless you specifically need a lightweight Spanish base model.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.30/10. License (MIT), params (124M small GPT-2), context (1024), and vendor all verify cleanly against the HF card. The description and verdict are honest and operator-voiced — explicitly telling readers to skip unless they have a narrow use case is exactly the right tone. Brand fit is the weakest axis since this is a 2020-era base model with limited practical local-AI value today, but the verdict acknowledges this directly rather than hyping it. Weaknesses correctly flag the lack of instruction tuning, tight context, and unfiltered training data.
Flags: - Marginal brand fit — model is dated and the row itself recommends skipping it for most use cases; justifiable only as a reference entry for Spanish-language local-AI builders
Overview
GPT-2 Spanish is a 124M-parameter model trained from scratch on 11.5GB of Spanish text (Wikipedia, books) with a custom Spanish BPE tokenizer. It generates Spanish prose but is not instruction-tuned — it completes text, it does not follow prompts. Context window is 1024 tokens.
Strengths
- Trained from scratch on Spanish data, not translated or adapted from English
- Custom BPE tokenizer built for Spanish vocabulary
- 124M parameters — runs on CPU or minimal VRAM
- MIT license, fully commercial-use friendly
Weaknesses
- GPT-2 architecture is several generations behind current open models
- 1024-token context is tight for anything beyond short completions
- No instruction tuning — useless for chat or task-following out of the box
- Trained on unfiltered data; offensive or low-quality outputs are possible
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 | 0.1 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 GPT-2 Spanish.
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 GPT-2 Spanish?
Can I use GPT-2 Spanish commercially?
What's the context length of GPT-2 Spanish?
Source: huggingface.co/DeepESP/gpt2-spanish
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify GPT-2 Spanish runs on your specific hardware before committing money.