AnthonyFC/langlm-gguf

langlm: grammar correction for learners of German and Spanish

Two small models that correct a learner's sentence and say what kind of error each fix was. They are fine-tuned from EuroLLM-1.7B, compressed to about 755 MB each, and fast enough to run offline on a laptop or inside a browser tab.

File	Language	Size	Parameters
langlm-de-755mb.gguf	German	755 MiB	1.294B
langlm-es-q4km.gguf	Spanish	757 MiB	1.296B

Code, training pipeline and every report the numbers below come from: github.com/afchatfield/language_lm.

Results

F0.5 is MaxMatch (M²) over corrected sentences, the standard GEC metric, which weights precision twice as heavily as recall. Overcorrection is the share of 400 already-correct published sentences (news and Wikipedia) that the system changed at all. Lower is better: a tool that "fixes" correct text teaches a learner the wrong thing.

German: Falko-MERLIN

Held-out test split, 2,337 sentences, 5,963 gold edits:

System	P	R	F0.5	Overcorrection
LanguageTool (with n-grams)	0.5890	0.2091	0.4321	28.7%
EuroLLM-1.7B zero-shot	0.4634	0.0880	0.2501	10.0%
EuroLLM-1.7B few-shot (8 examples)	0.6431	0.2574	0.4948	15.2%
langlm-de (before compression)	0.7322	0.6176	0.7060	9.5%

Compression, on the dev split (2,503 sentences):

Variant	F0.5	Overcorrection	Size
Uncompressed	0.7072	9.8%	997 MB
Vocabulary-trimmed (this file)	0.7061	9.5%	755 MB

Spanish: COWS-L2H

Held-out test split, 4,284 sentences, 7,333 gold edits:

System	P	R	F0.5	Overcorrection
LanguageTool	0.4547	0.1630	0.3348	27.3%
EuroLLM-1.7B zero-shot	0.4632	0.0610	0.1997	11.2%
EuroLLM-1.7B few-shot (8 examples)	0.6092	0.1210	0.3371	8.0%
langlm-es (before compression)	0.6878	0.4867	0.6353	6.0%

Compression, on the dev split:

Variant	P	R	F0.5	Overcorrection	Size
Uncompressed	0.6510	0.4531	0.5987	6.0%	997 MB
Vocabulary-trimmed (this file)	0.6453	0.4534	0.5949	7.5%	757 MB

Which numbers belong to which model. The test-split rows were measured on each model before compression, and the test split was read once, to choose which model to compress. The files here are the compressed versions, and they were scored on dev only. On dev, compression costs German 0.0011 of F0.5 (95% CI straddles zero) and Spanish 0.0038 (CI [−0.0068, −0.0009]), plus 1.5 points of overcorrection. Across the five German models that have both scores, dev and test agree to within 0.005.

Speed

Setting	Tokens/s	One sentence
German, llama.cpp, Metal, M1 16 GB	62.3	~0.6 s
German or Spanish, browser (wllama, WebGPU), M1	13–16.5	3–5 s
Same, browser, CPU only	2.8	~22 s

Peak memory for the German model under llama.cpp is 477 MB.

What each model is

Both models share one recipe, run once per language:

1. Base: EuroLLM-1.7B. It was chosen over Qwen3-1.7B and Qwen3.5 because it has the best German tokenizer fertility (1.69 tokens/word) and the most vocabulary left unused by any one language.
2. Fine-tune: LoRA (r=32, alpha=64, all linear layers), merged into the base. The loss covers the answer only, and the changes list is down-weighted to 0.25 because F0.5 never reads it. In the German tuning runs that was the largest gain after switching to real learner data.
3. Vocabulary trim: EuroLLM's 128,000-row vocabulary is cut to the rows that cover 99.9% of the language's text, with English and the task's own prompts and answers counted too, so the JSON schema survives. That keeps 39,399 rows for German and 39,886 for Spanish and removes about 22% of the parameters. The LoRA touches no vocabulary-sized weight, so the adapter carries over with no retraining.
4. Quantisation: Q4_K_M with llama.cpp.

German training data: the 19,237 real learner sentences in Falko-MERLIN train, plus errors injected by rule into clean German news and Wikipedia text from the Leipzig corpora. The injected types are weighted to match the learner corpus's own error histogram. About 22% of the examples need no correction, which matches Falko-MERLIN's measured rate and is what teaches the model to leave correct text alone.

Spanish training data: 97,473 examples, made up of 37,473 real learner sentences from COWS-L2H (essays by university learners of Spanish) and 60,000 with rule-injected errors. 33% need no correction, which is COWS-L2H's own rate.

No output from any other LLM provider was used at any stage.

How to use it

These are base-format checkpoints, not chat models. Send the exact prompt below as a raw completion. A chat template, which llama-cli applies by default, makes the model answer in prose instead of JSON.

Correct the German sentence, then list what changed.

Input: Der Mann hat ein grosse Haus gekauft .
Output:

For Spanish, the first line is Correct the Spanish sentence, then list what changed.

The input should be tokenised, with a space before punctuation, as in the training data. Decode greedily. The model answers with one line of JSON, the corrected sentence first:

{"correction": "Der Mann hat ein großes Haus gekauft .", "changes": [{"was": "grosse", "now": "großes", "type": "R:ADJ:FORM"}]}

type is an ERRANT error code: an operation (M missing, R replaced, U unnecessary), a word class, and :FORM when the problem is an inflection. The repository renders these into learner-facing explanations from rules/{de,es}_types.yaml.

With llama.cpp:

llama-server -m langlm-de-755mb.gguf -c 1024 -ngl 99 --port 8080

curl http://127.0.0.1:8080/completion -d '{
  "prompt": "Correct the German sentence, then list what changed.\n\nInput: Ich habe gestern ein Buch gelest .\nOutput:",
  "n_predict": 200, "temperature": 0
}'

In a browser, wllama loads these files directly. The repository's demo/ is a working example.

Limitations

• One sentence at a time. The models were trained on single sentences, so split paragraphs first.
• Learner text only. German was evaluated on Falko-MERLIN essays and Spanish on COWS-L2H essays. The models have not been measured on other kinds of writing.
• Error types are the weak part. The model's type matches the annotator's on 69% of German edits. Re-deriving the type from the (input, correction) pair with ERRANT gets 89% of the edits it segments the same way, and the repository's CLI does that. Treat type as a hint.
• Some errors are hard for it. For example, Spanish R:OTHER (wrong word choice) has 0.23 recall, and the per-type tables are in the repository.
• The Spanish trim is nearly free, not free. It touches 6 more of the 400 already-correct sentences than the uncompressed model does.
• Language-specific vocabularies. Each model's vocabulary is trimmed to its own language. The other language still encodes through byte fallback, but at 40–56% more tokens and with no measured accuracy. Use the matching model.

Licence and data

The base model, EuroLLM-1.7B, is Apache-2.0. The fine-tuning data carries its own terms. Falko is CC BY 3.0 and MERLIN is CC BY-SA 4.0. The clean German and Spanish text that synthetic errors were injected into comes from the Leipzig Corpora Collection, which is CC BY-NC 4.0, and that is why these weights are released for non-commercial use. COWS-L2H is used under its own release terms.