GPT-2 Spanish Medium
A 355M-parameter GPT-2 Medium trained from scratch on 11.5 GB of Spanish text (Wikipedia and books), with a BPE tokenizer built specifically for Spanish. Context window is 1024 tokens. Training data was not filtered for offensive or discriminatory content.
This model made sense in 2020; in 2024 it is mostly a fine-tuning base or a research curiosity. The unfiltered training data is a real deployment risk — do not put this in front of users without a content layer on top. If you need a small Spanish-capable model for prototyping or continued pre-training, it is a serviceable starting point, but skip it for anything production-facing.
›Why this rating
Auto-generated rating (Opus 4.7 judge, claude-opus-4-7). Overall 9.20/10. License (MIT), parameter count (355M matches GPT-2 medium), context (1024), and vendor are all verifiable directly from the model card. The editorial voice is honest and appropriately calls out the unfiltered training data and the model's age. The verdict is operator-grade — it explicitly tells readers to skip this for production. Brand fit is borderline since this is a 2020-era GPT-2 with limited practical use beyond fine-tuning bases, but the row is honest about that, which preserves catalog integrity.
Flags: - Marginal brand fit — older research-tier model with niche practical value; row's honesty about this is what saves it - bestUseCase could be slightly sharper (e.g., 'Spanish-language fine-tuning base for narrative/literary text')
Overview
A 355M-parameter GPT-2 Medium trained from scratch on 11.5 GB of Spanish text (Wikipedia and books), with a BPE tokenizer built specifically for Spanish. Context window is 1024 tokens. Training data was not filtered for offensive or discriminatory content.
Strengths
- Trained from scratch on Spanish — not translated or adapted from English weights
- Custom BPE tokenizer tuned for Spanish morphology
- 11.5 GB training corpus spanning Wikipedia and books
- MIT license, commercial use permitted
Weaknesses
- 1024-token context is tight by current standards
- Training data unfiltered — model can produce offensive or discriminatory output
- 355M parameters is small compared to modern capable models
- Low community traction: 2,753 downloads and 9 likes on HF suggests limited real-world validation
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 | 0.2 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 Medium.
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 Medium?
Can I use GPT-2 Spanish Medium commercially?
What's the context length of GPT-2 Spanish Medium?
Source: huggingface.co/DeepESP/gpt2-spanish-medium
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify GPT-2 Spanish Medium runs on your specific hardware before committing money.