GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
UNI-PIC
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
UNI-PIC
Model Tree
Qwen/Qwen3.5-4B-Base
Input Modalities
Output Modalities
Supported Functionality
| Developed by | President Information Corp. (統一資訊, PIC) and Uni-President Information Philippines Corp. (UNI-PIPC) |
| Model type | Decoder-only causal language model (hybrid Gated DeltaNet–Gated Attention, as in Qwen3.5) |
| Base model | Qwen/Qwen3.5-4B-Base |
| Adaptation method | Continual pre-training (CPT) |
| Domain | Taiwan retail and e-commerce |
| Languages | Traditional Chinese (zh-TW, primary), Simplified Chinese (zh-CN), English (en) |
| Training framework | ms-swift |
| Training hardware | 4× NVIDIA H100 96 GB |
| License | Apache 2.0 |
Token counts are computed with the base model tokenizer. A 0.1% split of each dataset is held out for validation.
53.7% · 1.38B Taiwan Retail Wiki26.3% · 674.4M Replay Data20.0% · 512.9M Replay Composition (% of Replay Tokens) zh-TW Wikipedia40.0% en↔zh Translation Pairs30.0% en Wikipedia20.0% zh-CN Wikipedia10.0% Product Listings1.38Btokens7,300,108 samples · 53.7% Taiwan Retail Wiki674.4Mtokens2,762,847 samples · 26.3% Replay Data512.9Mtokens2,774,330 samples · 20.0% Total2.56Btokens12,837,285 samples · 100% Taiwan retail wiki (synthetic, NVIDIA NeMo Data Designer): brand and contributor wikis, plus name variants (aliases, foreign-language names, Simplified/Traditional script, Zhuyin and Pinyin spellings, typos). Replay: general-domain text mixed into CPT to mitigate catastrophic forgetting. Sources: wikimedia/wikipedia (zh-TW, zh-CN, en) and en↔zh translation pairs from wmt/wmt19. zh-TW = Chinese converted to Traditional Chinese with OpenCC (translation pairs: Chinese side converted to zh-TW).
The product listings are proprietary Taiwan retail data comprising title, brand, category, price, and description. The data was cleaned with NVIDIA NeMo Curator, which removed SEO filler text, emojis, contact details, hyperlinks, and policy terms, followed by exact and fuzzy deduplication.
Category distribution (15 categories, share of listings)
The CPT model is compared against its base model, Qwen/Qwen3.5-4B-Base. Statistical significance is assessed with a 95% paired bootstrap over items (1,000 resamples):
🟢 significant improvement · 🔴 significant degradation · ⚪ no significant difference
Metric: Bits per byte (BPB); lower is better.
1.3920.359−1.032 Retail Wiki (Brand & Contributor)1.0110.574−0.437 Name Variants (Brand & Contributor)1.1430.348−0.795
0.9290.854−0.074 Chinese (Simplified)0.9310.900−0.031 English0.6840.647−0.038 English–Chinese Translation0.8100.541−0.270
Metric: PMI-normalized accuracy, which adjusts for the prior likelihood of each option; higher is better.
Setup: Zero-shot, five-option multiple choice (chance level: 20%). Each option is scored by its log-likelihood as a continuation of the prompt.
Data: Internal probe sets derived from the retail training data. Alias and wiki-fact labels are LLM-derived. Scores are not comparable with results reported for other models.
50.5%64.9%+14.4% Retail Wiki Knowledge54.6%65.6%+10.9% Name Variants (Brand & Contributor)78.6%83.6%+5.0%
These probes test knowledge about products and about the works (books, music, and other media) that products represent.
61.4%69.9%+8.5% Product Title → Brand (Brand Masked)31.6%34.5%+2.9% Product → Category64.7%84.3%+19.7% Brand → Main Category41.4%65.0%+23.6% Work → Contributor53.7%70.8%+17.2%
A contributor is a credited person or organization on a product or work, such as an author, singer, artist, illustrator, translator, editor, or publisher. Brand origin is the country or region where the brand was founded.
48.2%51.9%+3.7% Brand: Founding Year57.4%66.5%+9.1% Brand: Origin64.6%74.0%+9.4% Brand: Parent Company60.6%71.5%+10.9% Contributor: Nationality76.8%88.5%+11.8% Contributor: Occupation53.0%64.5%+11.5% Contributor: Credit Role21.8%42.0%+20.3%
A name variant is an alternative form of a brand or contributor name: a foreign-language alias, a different script, a phonetic spelling, or a typographical error. The canonical name is the standard form of the name.
Query Normalization (Variant → Canonical Name). Given a noisy variant of a brand or contributor name, the model selects the canonical name. A typographical error is either a Latin-letter typo or a same-sound Chinese character substitution.
66.7%70.9%+4.2% Typographical Error90.2%86.3%−3.9% Simplified Chinese Script98.7%91.0%−7.7% Zhuyin (Bopomofo)66.1%69.8%+3.7% Zhuyin Keystroke Sequence20.6%19.6%−1.0% Pinyin (Toneless)92.4%84.3%−8.1% Pinyin (Tone Numbers)89.5%80.4%−9.2%
Variant Generation (Canonical Name → Variant). Given a canonical brand or contributor name, the model selects the correct variant.
79.6%90.0%+10.4% Simplified Chinese Script100.0%100.0%+0.0% Zhuyin (Bopomofo)98.9%100.0%+1.1% Zhuyin Keystroke Sequence20.4%95.0%+74.6% Pinyin (Toneless)99.6%100.0%+0.4% Pinyin (Tone Numbers)99.6%99.9%+0.3%
bibtex
@misc{retailtwqwen354bbase, title = {Retail-TW-Qwen3.5-4B-Base}, author = {President Information Corp. (PIC) and Uni-President Information Philippines Corp. (UNI-PIPC)}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/UNI-PIC/Retail-TW-Qwen3.5-4B-Base}}@misc{retailtwqwen354bbase, title = {Retail-TW-Qwen3.5-4B-Base}, author = {President Information Corp. (PIC) and Uni-President Information Philippines Corp. (UNI-PIPC)}, year = {2026}, publisher = {Hugging Face}, url = {https://huggingface.co/UNI-PIC/Retail-TW-Qwen3.5-4B-Base}}