62 lines
2.1 KiB
Markdown
62 lines
2.1 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen3-0.6B-Base
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language:
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- ko
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- qwen3
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- vocabulary-trimming
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- trimming
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- korean
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- english
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---
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# Qwen3-0.6B-Base — vocabulary-trimmed (Korean + English, 32,768)
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A vocabulary-**trimmed** version of [`Qwen/Qwen3-0.6B-Base`](https://huggingface.co/Qwen/Qwen3-0.6B-Base):
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the byte-level BPE vocabulary is reduced from ~151.7k to **32768** tokens covering **Korean + English**,
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and the (tied) embedding matrix is sliced to match. **No retraining** — weights are copied verbatim for
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kept tokens, so on kept tokens the model is numerically identical to the original.
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Method: [Introduction to Trimming](https://huggingface.co/blog/lbourdois/introduction-to-trimming).
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## What changed
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| | Original | Trimmed |
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|---|---|---|
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| Parameters | 596,049,920 | 474,021,888 (**-20.5%**) |
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| Vocab size | 151,669 | 32,768 |
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| Merges | 151,387 | 32,486 |
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| Embedding | tied | tied (sliced) |
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- All 256 byte-level tokens + all 26 special tokens are kept, plus the most frequent Korean/English
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tokens **and their full BPE merge-derivation closure** (so multi-byte Korean stays reachable).
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- Verified: lossless round-trip on KO/EN/code; teacher-forced logit equivalence `max|Δ| = 0.0`; greedy
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generation on natural Korean/English is **token-for-token identical** to the base model.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "ceyda/Qwen3-0.6B-Base-trim-koen-32768"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo)
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ids = tok("대한민국의 수도는", return_tensors="pt")
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print(tok.decode(model.generate(**ids, max_new_tokens=20)[0], skip_special_tokens=True))
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```
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## Limitations
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- Trimmed for **Korean + English prose**. Code and rare jargon still encode losslessly but may split
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into a few extra tokens. Text in other languages falls back to byte tokens (longer sequences).
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- This is a **base** (non-instruction-tuned) model.
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## Attribution
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Derived from `Qwen/Qwen3-0.6B-Base` (Apache-2.0). Trimming method by Loïck Bourdois.
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