jaigouk/smollm3-german-teacher

SmolLM3 German Teacher V6 (λ=2500)

A finetuned 3B parameter model specialized for teaching German at A1-B1 levels, optimized for grammatical accuracy using Elastic Weight Consolidation (EWC).

Model Details

Evaluation Results

Why V6?

We tested multiple approaches (V5-V9) to balance accuracy and fluency:

V6 has the best grammatical accuracy, which is critical for a German teacher model. Preference optimization (V8/V9) improved fluency but destroyed accuracy.

Capabilities

• —German grammar correction with clear explanations
• —Error identification: haben/sein in Perfekt, article gender, case endings, word order
• —A1-B1 level instruction following CEFR guidelines
• —Bilingual explanations (German/English)

Files

Usage

With Ollama

# Download and create model
ollama create smollm3-german:v6 -f - <<EOF
FROM ./smollm3-german-teacher-v6-q4_k_m.gguf

TEMPLATE """{{- if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{- range .Messages }}<|im_start|>{{ .Role }}
{{ .Content }}<|im_end|>
{{ end }}<|im_start|>assistant
"""

SYSTEM """You are a friendly German language teacher specializing in A1-B1 level instruction. Help students learn German grammar, correct mistakes with clear explanations, and provide examples."""

PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.7
EOF

# Use
ollama run smollm3-german:v6 "Korrigiere: Ich habe nach Berlin gefahren."

With llama.cpp

./llama-cli -m smollm3-german-teacher-v6-q4_k_m.gguf \
  -p "<|im_start|>user\nKorrigiere: Ich habe nach Berlin gefahren.<|im_end|>\n<|im_start|>assistant\n" \
  -n 256 --temp 0.7

With Transformers (Full Model)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("jaigouk/smollm3-german-teacher")
tokenizer = AutoTokenizer.from_pretrained("jaigouk/smollm3-german-teacher")

prompt = "Korrigiere: Ich habe nach Berlin gefahren."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

Example Output

Input: Korrigiere: Ich habe nach Berlin gefahren.

Output:

Die korrekte Form ist: "Ich bin nach Berlin gefahren." Erklärung: Bei Verben der Bewegung (fahren, gehen, kommen, fliegen) verwendet man im Perfekt das Hilfsverb "sein", nicht "haben". Regel: Bewegungsverben + sein = Perfekt - Ich bin gefahren (nicht: Ich habe gefahren) - Ich bin gegangen - Ich bin gekommen

Training Details

Two-Stage EWC Training

1. 1.Stage 1: Grammar fundamentals (articles, cases, verb conjugation)
2. 2.Stage 2: Error correction and explanations with EWC regularization

EWC (Elastic Weight Consolidation)

EWC prevents catastrophic forgetting by:

• —Computing Fisher Information Matrix after Stage 1
• —Using Fisher as importance weights during Stage 2
• —Loss: L_total = L_task + λ * L_ewc

Lambda=2500 was selected from grid search over [2500, 5000, 10000].

Hyperparameters

# Stage 1 & 2
learning_rate = 2e-5
batch_size = 4
epochs = 3
lora_r = 16
lora_alpha = 32

# EWC
ewc_lambda = 2500
fisher_samples = 500

Limitations

• —Optimized for A1-B1 level; may struggle with advanced grammar (C1+)
• —Generation fluency is lower than V8/V9 (trade-off for accuracy)
• —Best for structured grammar tasks; less natural in open conversation
• —German/English bilingual; other languages not supported

Citation

@misc{smollm3-german-teacher-v6,
  author = {Jaigouk Kim},
  title = {SmolLM3 German Teacher V6: EWC-Optimized Grammar Instruction Model},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/jaigouk/smollm3-german-teacher}
}

Acknowledgments

• —HuggingFace for SmolLM3 base model
• —Unsloth for efficient QLoRA training
• —llama.cpp for GGUF conversion

License

Apache 2.0 (same as base model)