Instructions to use foksly/wmt26-constrained-submission with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foksly/wmt26-constrained-submission with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="foksly/wmt26-constrained-submission")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("foksly/wmt26-constrained-submission")
model = AutoModelForCausalLM.from_pretrained("foksly/wmt26-constrained-submission", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use foksly/wmt26-constrained-submission with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "foksly/wmt26-constrained-submission"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "foksly/wmt26-constrained-submission",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/foksly/wmt26-constrained-submission

SGLang

How to use foksly/wmt26-constrained-submission with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "foksly/wmt26-constrained-submission" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "foksly/wmt26-constrained-submission",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "foksly/wmt26-constrained-submission" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "foksly/wmt26-constrained-submission",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use foksly/wmt26-constrained-submission with Docker Model Runner:
```
docker model run hf.co/foksly/wmt26-constrained-submission
```

8B submission to the WMT26 General MT task

This repository contains our constrained submission to the WMT26 General Machine Translation task. The model translates English into Russian, Belarusian, Kazakh, and Armenian. It is an approximately 8B-parameter decoder-only causal language model.

Model details

Property	Value
Model type	Decoder-only causal language model
Parameters	Approximately 8B
Source language	English
Target languages	Russian, Belarusian, Kazakh, Armenian
Context length	32,768 tokens
License	See `LICENSE`

Usage

The repository includes inference.py, which supports plain translation prompts, the four WMT26 domain prompts, and custom instructions. It uses greedy decoding and prints only the generated translation to standard output.

Install the required packages:

python -m pip install "torch>=2.1" "transformers>=4.46.3,<5" accelerate sentencepiece packaging

Translate a string into Russian:

python inference.py \
  --target ru \
  --text "The agreement will enter into force next month."

Use one of the WMT26 domain instructions:

python inference.py \
  --target kk \
  --domain news \
  --text "The committee announced the results on Tuesday."

The supported domain values are social, speech, news, and software. The source text can also be supplied through standard input. Use --prompt or --prompt-file to provide a custom instruction.

Transformers example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "foksly/wmt26-constrained-submission"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
).eval()

prompt = """Переведи с английского на казахский:
The committee announced the results on Tuesday."""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=512, do_sample=False)
print(tokenizer.decode(
    output[0, inputs["input_ids"].shape[1]:],
    skip_special_tokens=True,
))

Evaluation

We evaluate the released checkpoint against five public models with at most 20B parameters. These are local evaluation results, not official WMT26 scores.

BOUQuET

The following paragraph-level results use the BOUQuET references. MetricX uses the google/metricx-24-hybrid-xxl-v2p6 checkpoint.

ChrF++ (higher is better)

System	en-ru	en-be	en-kk	en-hy
Our Model	62.3	58.0	55.3	53.5
TranslateGemma-12B	62.2	54.9	47.9	50.1
Qwen-3.5-9B	60.1	49.1	48.3	49.6
MADLAD-400-10B	59.4	54.2	46.8	50.3
NLLB-200-3.3B	57.4	52.1	47.8	52.8
GPT-OSS-20B	51.9	29.3	49.0	12.9

MetricX-24-XXL (lower is better)

System	en-ru	en-be	en-kk	en-hy
Our Model	1.58	3.87	3.44	5.41
TranslateGemma-12B	1.52	3.83	5.60	6.30
Qwen-3.5-9B	2.22	5.73	5.52	7.32
MADLAD-400-10B	4.79	5.56	5.86	7.13
NLLB-200-3.3B	6.01	7.63	7.10	6.35
GPT-OSS-20B	2.72	6.16	5.82	9.18

WMT25 General MT source paragraphs

We also translate the official English source paragraphs from the WMT25 General MT task and evaluate them with ORBIT-SC using GPT-5.4 as a single judge. The MQM score is computed as 5 × Major + Minor from the predicted error spans.

Accuracy (higher is better)

System	en-ru	en-be	en-kk	en-hy
Our Model	81.6	69.1	69.9	52.2
TranslateGemma-12B	73.9	59.1	47.2	41.8
Qwen-3.5-9B	69.6	51.1	46.2	39.1
MADLAD-400-10B	32.5	40.5	40.8	28.3
NLLB-200-3.3B	39.7	33.8	32.2	35.0
GPT-OSS-20B	62.4	40.9	40.8	30.2

Fluency (higher is better)

System	en-ru	en-be	en-kk	en-hy
Our Model	85.3	69.5	72.0	54.0
TranslateGemma-12B	81.2	64.4	52.5	48.1
Qwen-3.5-9B	72.7	51.8	51.4	44.8
MADLAD-400-10B	32.5	44.8	48.4	37.5
NLLB-200-3.3B	40.3	33.8	35.7	36.4
GPT-OSS-20B	63.5	39.3	42.3	29.4

MQM (lower is better)

System	en-ru	en-be	en-kk	en-hy
Our Model	11.3	24.2	22.3	37.6
TranslateGemma-12B	17.9	30.8	40.9	43.8
Qwen-3.5-9B	21.4	38.5	40.8	46.4
MADLAD-400-10B	34.5	38.6	40.1	31.7*
NLLB-200-3.3B	39.5	46.0	45.5	46.2
GPT-OSS-20B	27.2	46.8	44.5	52.4

* The MADLAD English-to-Armenian MQM value is affected by the count-based aggregation of a small number of long critical spans. Its low value should not be interpreted as strong translation quality.

License

The model is distributed under the terms in LICENSE. Review the license before using or redistributing the model.

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Safetensors

Model size

8B params

Tensor type

F16