GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
Neobe
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
Neobe
Model Tree
Qwen/Qwen3-4B
Input Modalities
Output Modalities
Supported Functionality
| Benchmark | chrF | chrF++ | BLEU |
|---|---|---|---|
| gold (human references, article-level, N=500) | 49.34 | 41.11 | 4.12 |
| held-out chunk (in-distribution) | 67.17 | 59.98 | 20.87 |
| held-out sentence (in-distribution) | 60.58 | 53.54 | 15.91 |
chrF is the metric to trust for Thaana; BLEU is unreliable (word segmentation / morphology).
Input (en): While this is a 7.6 percent increase compared to the same period last year, an average of 7,778 tourists visit the Maldives daily.
Output (dv): މިއީ، ވޭތުވެދިޔަ އަހަރުގެ މި މުއްދަތާ ބަލާއިރު 7.6 ޕަސެންޓުގެ ކުރިއެރުމެއް ކަމަށްވާއިރު، ދުވާލަކު އެވްރެޖްކޮށް 7،778 ފަތުރުވެރިން ރާއްޖެ ޒިޔާރަތްކުރެއެވެ.
Real held-out sample and this model's own output.
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelbase, adapter = "Qwen/Qwen3-4B", "Neobe/en-dhivehi-qwen3-4b-lora-sentence"tok = AutoTokenizer.from_pretrained(adapter)model = PeftModel.from_pretrained(AutoModelForCausalLM.from_pretrained(base, torch_dtype=torch.bfloat16, device_map="cuda"),adapter).eval()src = "The President of the Maldives met with the cabinet today."msgs = [{"role":"user","content":"Translate the following English text to Dhivehi. Output only the translation, "f"no explanations.\n\nEnglish: {src}\nDhivehi:"}]prompt = tok.apply_chat_template(msgs, tokenize=False, add_generation_prompt=True)inp = tok(prompt, return_tensors="pt", truncation=True, max_length=3072).to("cuda")out = model.generate(**inp, max_new_tokens=1024, num_beams=1, repetition_penalty=1.15, no_repeat_ngram_size=0, length_penalty=1.0, do_sample=False)print(tok.decode(out[0][inp["input_ids"].shape[1]:], skip_special_tokens=True))
~9–10 GB VRAM (bf16). Qwen tokenizes Thaana at ~1.8 tokens/char — don't over-truncate the input.
Base Qwen/Qwen3-4B; LoRA r=16, α=32, targets q/k/v/o+gate/up/down; bf16; adamw_torch LR 2e-4 cosine; max_length 1536; 1 epoch; effective batch ~32; gradient checkpointing.
Domain = Maldivian news / press / Wikipedia; technical or informal English is out of distribution. Non-human references are machine-generated (distillation).
bibtex
@misc{neobe_en_dhivehi_qwen3_4b_lora_sentence_2026,title = {Qwen3-4B English→Dhivehi (sentence-level)},author = {Neobe},year = {2026},howpublished = {\url{https://huggingface.co/Neobe/en-dhivehi-qwen3-4b-lora-sentence}}}