iapp

ChindaMT-4B

README

License: apache-2.0

Prompting

Plain translation. Same template for both directions; swap the language line and the source tag:

text

Translate English to Thai.EN: The weather is nice today.

text

Translate Thai to English.TH: วันนี้อากาศดีมาก

With rules. Add a Rules: block between the language line and the source line. Rules are free-form text:

text

Translate English to Thai.Rules:- Return only the translated text- Use a clear, professional tone in Thai- Keep all numerals in Arabic digitsEN: <source text>

Inference

Requires transformers 5.2 or newer:

bash

pip install -U "transformers>=5.2"

python

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "iapp/ChindaMT-4B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") prompt = "Translate English to Thai.\n\nEN: The weather is nice today."messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(    messages, add_generation_prompt=True, tokenize=False, enable_thinking=False,)inputs = tokenizer(text, return_tensors="pt").to(model.device)out = model.generate(    **inputs, max_new_tokens=1024, temperature=0.01, top_p=0.7, top_k=20,    repetition_penalty=1.05, do_sample=True,)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Thinking mode is disabled (enable_thinking=False); all reported results use this setting.

Training

Method: full-parameter supervised fine-tuning with LLaMA-Factory, DeepSpeed ZeRO-2
Data: Grounded, 1.97M records
Epochs: 1
Learning rate: 2e-5, inverse-square-root schedule, 1% warmup
Optimizer: AdamW (0.9, 0.999), weight decay 0.01
Effective batch size: 64, on two H100 GPUs
Cutoff length: 1024 tokens
Seed: 42
The same recipe trains all three sizes.

Decoding settings

All reported results, and the inference snippet above, use these settings:

temperature 0.01, top-p 0.7, top-k 20, repetition penalty 1.05
max new tokens 1024
thinking mode off (enable_thinking=False)
input length: trained with a 1024-token cutoff. Longer inputs worked in our tests but are used at your own risk; recommended: translate long documents paragraph by paragraph, and raise max_new_tokens if an output is cut short.

Evaluation

How to read the tables

Metric: length-controlled pairwise win rate (LC%), AlpacaEval-v2 protocol, 400 items per split. Each cell is the LC% of ChindaMT-4B against the model named in that row, so values above 50 mean ChindaMT-4B wins and 50 is a tie.
Judge: Qwen3.6-35B, with two cross-judges from other model families as a check (below).
Plain (shared): both systems get the same prompt scaffold, translation only.
Plain (own template): the baseline uses its own recommended prompt, the hardest comparison for ChindaMT-4B.
Constrained: the prompt adds a rules block of one to four constraints; wins here reflect rule-following as well as translation quality.
Dashes: omitted because the comparator returns almost no target-language output under the shared prompt.

Against its own base (Qwen3.5-4B): Plain 55.7, Constrained 62.2 on CoreEval.

CoreEval (five deployment domains):

Table with columns: ChindaMT-4B vs, Plain (shared), Plain (own template), Constrained
ChindaMT-4B vs	Plain (shared)	Plain (own template)	Constrained
MiLMMT-46-4B	86.3	53.3	89.5
TranslateGemma-4B	81.0	66.7	87.2
Typhoon-Translate-1.5-4B	61.8	57.8	68.4
HY-MT-1.5-7B	74.0	62.9	97.9

BroadEval (ten broader domains, cross-domain generalization):

Table with columns: ChindaMT-4B vs, Plain (shared), Plain (own template), Constrained
ChindaMT-4B vs	Plain (shared)	Plain (own template)	Constrained
MiLMMT-46-4B	-	56.2	-
TranslateGemma-4B	81.8	67.0	76.1
Typhoon-Translate-1.5-4B	58.7	53.1	63.2
HY-MT-1.5-7B	70.1	62.0	98.2

What the numbers mean

Each number is a win rate out of 100: how often the judge preferred the translation from ChindaMT-4B over the other model's, on the same 400 sentences, after correcting for output length. 50 is a tie. For example, 61.8 against Typhoon-Translate-1.5-4B means the judge preferred ChindaMT-4B in about 62 of 100 head-to-head comparisons.
Under Constrained, a win means the translation was judged better and respected the rules, so those numbers measure rule-following as well as translation quality.
In short: ChindaMT-4B is preferred over its base and over every same-size baseline on both splits, with the widest margins under rules.

External metrics (direction-averaged; quality mean averages CometKiwi, GEMBA-DA, and GEMBA-MQM on a 0-100 scale, higher is better; MetricX-24 is an error score, lower is better):

Table with columns: Benchmark, Quality mean, MetricX-24 error
Benchmark	Quality mean	MetricX-24 error
FLORES-200 (Wikipedia)	90.7	2.06
WMT24++ en-th (news)	87.8	3.20

Human evaluation. Three native Thai raters preferred ChindaMT-4B over Typhoon-Translate-1.5-4B on 64% of Plain items (Plain with the baseline template) and 69% of Constrained items, inter-rater kappa 0.73.

Cross-judges. GPT-OSS-120B and Llama-3.3-70B-Instruct agree with the primary judge in direction on every Constrained and Plain (shared) comparison. Full protocol, standard errors, and all baselines are in the paper.

Evaluation suites

iapp/ChindaMT-CoreEval: 800 items, five deployment domains
iapp/ChindaMT-BroadEval: 800 items, ten broader domains

Limitations

Thai-English only, in both directions.
Rule types are those a reference translation can demonstrate: terminology, length, register, and output format. Mandated glossaries and placeholder tokens are outside the training signal by design.
The primary judge shares the Qwen family with the model; cross-judges from two other families and non-LLM metrics are reported in the paper as checks.
Input length. Trained with a 1024-token cutoff. Inputs above 1k tokens still translated cleanly in our tests, but use them at your own risk. Recommended: translate long documents paragraph by paragraph.

Citation

bibtex

@misc{chayintr2026rgdc,  title         = {Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation},  author        = {Chay-intr, Thodsaporn and Harnchang, Krittapad and Thabua, Mahannop and Viriyayudhakorn, Kobkrit and Theeramunkong, Thanaruk},  year          = {2026},  eprint        = {2609.34770},  archivePrefix = {arXiv},  primaryClass  = {cs.CL},  url           = {https://arxiv.org/abs/2609.34770},  note          = {Accepted at AACL-IJCNLP 2026 (Main Conference)}}

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README

License: apache-2.0

Prompting

Plain translation. Same template for both directions; swap the language line and the source tag:

text

Translate English to Thai.EN: The weather is nice today.

text

Translate Thai to English.TH: วันนี้อากาศดีมาก

With rules. Add a Rules: block between the language line and the source line. Rules are free-form text:

text

Translate English to Thai.Rules:- Return only the translated text- Use a clear, professional tone in Thai- Keep all numerals in Arabic digitsEN: <source text>

Inference

Requires transformers 5.2 or newer:

bash

pip install -U "transformers>=5.2"

python

from transformers import AutoTokenizer, AutoModelForCausalLMimport torch model_id = "iapp/ChindaMT-4B"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") prompt = "Translate English to Thai.\n\nEN: The weather is nice today."messages = [{"role": "user", "content": prompt}]text = tokenizer.apply_chat_template(    messages, add_generation_prompt=True, tokenize=False, enable_thinking=False,)inputs = tokenizer(text, return_tensors="pt").to(model.device)out = model.generate(    **inputs, max_new_tokens=1024, temperature=0.01, top_p=0.7, top_k=20,    repetition_penalty=1.05, do_sample=True,)print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Thinking mode is disabled (enable_thinking=False); all reported results use this setting.

Training

Method: full-parameter supervised fine-tuning with LLaMA-Factory, DeepSpeed ZeRO-2
Data: Grounded, 1.97M records
Epochs: 1
Learning rate: 2e-5, inverse-square-root schedule, 1% warmup
Optimizer: AdamW (0.9, 0.999), weight decay 0.01
Effective batch size: 64, on two H100 GPUs
Cutoff length: 1024 tokens
Seed: 42
The same recipe trains all three sizes.

Decoding settings

All reported results, and the inference snippet above, use these settings:

temperature 0.01, top-p 0.7, top-k 20, repetition penalty 1.05
max new tokens 1024
thinking mode off (enable_thinking=False)
input length: trained with a 1024-token cutoff. Longer inputs worked in our tests but are used at your own risk; recommended: translate long documents paragraph by paragraph, and raise max_new_tokens if an output is cut short.

Evaluation

How to read the tables

Metric: length-controlled pairwise win rate (LC%), AlpacaEval-v2 protocol, 400 items per split. Each cell is the LC% of ChindaMT-4B against the model named in that row, so values above 50 mean ChindaMT-4B wins and 50 is a tie.
Judge: Qwen3.6-35B, with two cross-judges from other model families as a check (below).
Plain (shared): both systems get the same prompt scaffold, translation only.
Plain (own template): the baseline uses its own recommended prompt, the hardest comparison for ChindaMT-4B.
Constrained: the prompt adds a rules block of one to four constraints; wins here reflect rule-following as well as translation quality.
Dashes: omitted because the comparator returns almost no target-language output under the shared prompt.

Against its own base (Qwen3.5-4B): Plain 55.7, Constrained 62.2 on CoreEval.

CoreEval (five deployment domains):

Table with columns: ChindaMT-4B vs, Plain (shared), Plain (own template), Constrained
ChindaMT-4B vs	Plain (shared)	Plain (own template)	Constrained
MiLMMT-46-4B	86.3	53.3	89.5
TranslateGemma-4B	81.0	66.7	87.2
Typhoon-Translate-1.5-4B	61.8	57.8	68.4
HY-MT-1.5-7B	74.0	62.9	97.9

BroadEval (ten broader domains, cross-domain generalization):

Table with columns: ChindaMT-4B vs, Plain (shared), Plain (own template), Constrained
ChindaMT-4B vs	Plain (shared)	Plain (own template)	Constrained
MiLMMT-46-4B	-	56.2	-
TranslateGemma-4B	81.8	67.0	76.1
Typhoon-Translate-1.5-4B	58.7	53.1	63.2
HY-MT-1.5-7B	70.1	62.0	98.2

What the numbers mean

Each number is a win rate out of 100: how often the judge preferred the translation from ChindaMT-4B over the other model's, on the same 400 sentences, after correcting for output length. 50 is a tie. For example, 61.8 against Typhoon-Translate-1.5-4B means the judge preferred ChindaMT-4B in about 62 of 100 head-to-head comparisons.
Under Constrained, a win means the translation was judged better and respected the rules, so those numbers measure rule-following as well as translation quality.
In short: ChindaMT-4B is preferred over its base and over every same-size baseline on both splits, with the widest margins under rules.

External metrics (direction-averaged; quality mean averages CometKiwi, GEMBA-DA, and GEMBA-MQM on a 0-100 scale, higher is better; MetricX-24 is an error score, lower is better):

Table with columns: Benchmark, Quality mean, MetricX-24 error
Benchmark	Quality mean	MetricX-24 error
FLORES-200 (Wikipedia)	90.7	2.06
WMT24++ en-th (news)	87.8	3.20

Evaluation suites

iapp/ChindaMT-CoreEval: 800 items, five deployment domains
iapp/ChindaMT-BroadEval: 800 items, ten broader domains

Limitations

Thai-English only, in both directions.
Rule types are those a reference translation can demonstrate: terminology, length, register, and output format. Mandated glossaries and placeholder tokens are outside the training signal by design.
The primary judge shares the Qwen family with the model; cross-judges from two other families and non-LLM metrics are reported in the paper as checks.
Input length. Trained with a 1024-token cutoff. Inputs above 1k tokens still translated cleanly in our tests, but use them at your own risk. Recommended: translate long documents paragraph by paragraph.

Citation

bibtex

@misc{chayintr2026rgdc,  title         = {Reference-Grounded Data Curation for Instruction-Following Thai-English Machine Translation},  author        = {Chay-intr, Thodsaporn and Harnchang, Krittapad and Thabua, Mahannop and Viriyayudhakorn, Kobkrit and Theeramunkong, Thanaruk},  year          = {2026},  eprint        = {2609.34770},  archivePrefix = {arXiv},  primaryClass  = {cs.CL},  url           = {https://arxiv.org/abs/2609.34770},  note          = {Accepted at AACL-IJCNLP 2026 (Main Conference)}}

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