English, German, French, Spanish, Italian, Portuguese, Dutch, Polish, Swedish, Danish, Norwegian Bokmål, Finnish, Czech, Romanian, Hungarian, Catalan, Greek

Cyrillic Script

Russian, Ukrainian, Bulgarian, Serbian

Asian Languages

Chinese (Mandarin), Japanese, Korean, Hindi, Bengali, Urdu, Tamil, Telugu, Marathi, Thai, Vietnamese, Indonesian, Malay, Filipino

Middle Eastern

Arabic, Persian, Turkish, Hebrew

African

Swahili, Amharic, Yoruba

Other

Lithuanian, Slovenian, Estonian, Latvian, Slovak, Croatian, Azerbaijani, Kazakh, Uzbek

Grammar Markers Explained

The model outputs grammatical information using standard linguistic abbreviations:

Noun Markers (Gender-based Languages)

nm. = Masculine noun (e.g., "nm. roi, monarque" = king, monarch in French)
nf. = Feminine noun (e.g., "nf. maison, demeure" = house, dwelling in French)
nn. = Neuter noun (German, Russian) (e.g., "nn. Haus, Gebäude" = house, building)

Noun Markers (Non-gendered Languages)

n. = Noun (e.g., "n. house, home" in English)

Other Parts of Speech

adj. = Adjective (e.g., "adj. rapide, vite" = fast, quick)
adv. = Adverb (e.g., "adv. rapidement, vite" = quickly, fast)
pp = Past participle (e.g., "mangé → eaten, consumed (pp)")

Verb Forms

For conjugated verbs, the model provides:

Translation(s)
Tense/mood information in parentheses
Example: "venait → came, was coming (imparfait, il)" = imperfect tense, he

Usage

Basic Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "ikhou/dict-xs",
    torch_dtype="bfloat16",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("ikhou/dict-xs")

# Example: Get a French→English gloss
messages = [
    {
        "role": "system",
        "content": "You are a bilingual dictionary. Given a word/phrase in context, output a short gloss.\n\nRules:\n- One line only, no labels\n- Use grammar markers: nm./nf./nn. for gendered nouns, n. for others, adj., adv., verbs with tense info\n- 1-4 short translations, comma-separated\n- Apply markers based on definition language"
    },
    {
        "role": "user",
        "content": 'Expression: "maison"\nContext: Il habite dans une petite 【maison】 près de la mer.\nSource language: fra (French)\nDefinition language: eng (English)\n\nReturn the single-line gloss now.'
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=50,
    temperature=0.3,
    do_sample=True,
    top_p=0.9
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)  # Output: "nf. house, home"

Input Format

The model expects:

Expression: The word/phrase to define
Context: Sentence with the expression (use 【】 to highlight)
Source language: ISO 639-3 code (e.g., fra, eng, deu)
Definition language: ISO 639-3 code

Output Format

The model returns a single line with:

Grammar marker (nm./nf./nn./n./adj./adv./pp)
1-4 short translations/synonyms, comma-separated
For verbs: glosses + grammatical info in parentheses

Training Details

Training Data

Dataset: 1.7M synthetic dictionary entries
Sources: FineWeb (English), FineWeb-2 (49 other languages)
Generation: GPT-4-based teacher model for quality glosses
Filtering: Proper noun filtering, quality scoring

Training Configuration

Base Model: Qwen/Qwen3-0.6B
Training Type: Full fine-tuning (not LoRA)
Precision: bfloat16
Batch Size: 32 per device
Gradient Accumulation: 8 steps
Total Steps: 6,568
Learning Rate: AdamW with cosine schedule
Hardware: NVIDIA H100 (95GB)
Training Time: ~6 hours

Training Results

Final Loss: 1.30
Eval Loss: 1.34
Training thoroughly validated - no zero loss issues

Model Architecture

Architecture: Qwen3ForCausalLM
Parameters: 596M
Layers: 28 transformer layers
Hidden Size: 1024
Attention Heads: 16 (8 KV heads)
Context Length: 40,960 tokens (model max, trained on 512)
Vocabulary: 151,936 tokens

Limitations

Context: Works best with clear, simple contexts
Proper nouns: May struggle with names, places, brands
Rare languages: Better performance on high-resource languages
Multi-word phrases: Best for 1-6 token phrases
Ambiguity: Provides common meanings, may miss context-specific nuances

Ethical Considerations

Bias: Trained on web data which may contain biases
Not for sensitive applications: Dictionary glosses may have errors
Educational use: Best for learning and reference, not authoritative translation

License

Apache 2.0

Citation

bibtex

@misc{ikhou-dict-xs,
  author = {Ikhou},
  title = {Ikhou Dictionary Model (dict-xs)},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ikhou/dict-xs}}
}

Acknowledgments

Based on Qwen3-0.6B by Alibaba Cloud
Training data sourced from FineWeb and FineWeb-2
Trained with Hugging Face Transformers

Contact

For issues or questions, please open an issue on the model repository.