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Model: ceyda/Qwen3-0.6B-Base-trim-koen-32768
Source: Original Platform

README.md

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