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palette-lab
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Model Details
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palette-lab
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Input Modalities
Output Modalities
Supported Functionality
Kakao FunctionChat-Bench SingleCall (500 Korean items, 5 tool conditions), exact match on function name and arguments, scorer in the repo. Comparators run with identical tools and queries in their own documented formats.
| model | params | exact | 4_random | 4_close | 8_random | 8_close | all | name only |
|---|---|---|---|---|---|---|---|---|
| Songgot-M (2 epochs, v5 set) | 126M | 32.0 | 13.0 | 8.0 | 7.0 | 0.0 | 12.0 | 53.0 |
| Songgot (6B tokens, 2 epochs, v7 set) | 50M | 40.0 | 33.0 | 22.0 | 30.0 | 13.0 | 27.6 | 69.4 |
| Songgot-nano (1 epoch) | 39M | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| Needle 2 | 45M | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| FunctionGemma-270M | 270M | 3.0 | 5.0 | 1.0 | 1.0 | 1.0 | 2.2 | 36.2 |
| Qwen3-0.6B | 600M | 48.0 | 49.0 | 45.0 | 37.0 | 37.0 | 43.2 | 70.8 |
| Qwen3.5-0.8B | 800M | 51.0 | 48.0 | 41.0 | 52.0 | 34.0 | 45.2 | 73.6 |
Tokens per Hangul syllable on the same 100 queries: Songgot 0.90, Gemma 3 0.98, Qwen3 1.15, Needle 2 3.47.
Weights in this repo are Songgot, 6B tokens, 2 epochs, v7 set: 12 layers, about 50M parameters, pretrained on 8xH100 (Modal) on 6B tokens, post-trained on the v7 set (v6 plus a second teacher-synthesised round with confusable sibling tools), post-trained on the v2 tool-calling set. Call accuracy on FunctionChat-Bench SingleCall 27.6 percent (name only 69.4). GGUF exports (f16, Q8_0, Q4_K_M) are in this repo.
markdown
<|system|>[{"name": "set_alarm", "description": "알람을 설정합니다.", "parameters": {...}}]<|user|>내일 아침 7시에 알람 맞춰줘<|call|>{"name":"set_alarm","arguments":{"time":"07:00"}}<|end|>
Tokenizer: SentencePiece BPE, 32k, byte fallback (tokenizer.model). Use sentencepiece directly; the special tokens live inside the vocabulary.
fineweb-edu sample-10BT (ODC-By), Korean Wikipedia 20231101.ko (CC BY-SA 3.0; this model card carries the attribution and share-alike notice for that text), glaive-function-calling-v2 (Apache 2.0), template-generated Korean tool calls (Apache 2.0, in the repo). No closed-model outputs. FunctionChat-Bench was never used for training.
Single-call tool selection and argument extraction only. No multi-turn, no tool results, no free chat. Small models are finicky with rare tools and paraphrased values; validate every call in application code.