Koshur Kouter KS-EN v1

A Kashmiri ↔ English translation model fine-tuned from sarvamai/sarvam-translate (Gemma 3, 4.5B parameters). A QLoRA adapter has been merged into the base weights and published as a fully self-contained transformers-compatible checkpoint.

Model Details

Intended Use

Primary use cases

• Kashmiri ↔ English machine translation, in either direction.
• Downstream evaluation, comparison, and benchmarking of Kashmiri NLP systems.
• Manual review workflows on mixed-direction prompts.
• Direct loading via transformers without PEFT adapter merging.

Out-of-scope use

• Production-grade translation without human review.
• Long-form document translation (model is tuned on short, sentence-level pairs).
• Open-ended generation, dialogue, or any task other than translation.
• Safety-critical applications (medical, legal, financial advice).

Quickstart

Installation

pip install -U transformers accelerate sentencepiece safetensors torch

Load the model

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

repo_id = "Omarrran/koshur-kouter-ks-en_v1"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
).eval()

Translate

def first_nonempty_line(text: str) -> str:
    for line in text.splitlines():
        if line.strip():
            return line.strip()
    return text.strip()


def translate(source: str, direction: str, max_new_tokens: int = 48) -> str:
    instructions = {
        "ks2en": "Translate the text below to English. Return only the translation.",
        "en2ks": "Translate the text below to Kashmiri. Return only the translation.",
    }
    messages = [
        {"role": "system", "content": instructions[direction]},
        {"role": "user", "content": source},
    ]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )
    inputs = tokenizer(
        prompt, return_tensors="pt", truncation=True, max_length=1024
    ).to(model.device)

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            repetition_penalty=1.15,
            no_repeat_ngram_size=3,
            pad_token_id=tokenizer.eos_token_id,
        )

    suffix = outputs[0][inputs.input_ids.shape[1]:]
    return first_nonempty_line(tokenizer.decode(suffix, skip_special_tokens=True))


print(translate("Hello how are you doing today?.", "en2ks"))
print(translate("کٔشیر چھُ اکھ خوبصورت جٲی", "ks2en"))

A ready-to-run notebook is included at notebooks/colab_load_stage1_model.ipynb.

Recommended Decoding

The model is sensitive to decoding settings. The defaults shipped in generation_config.json are tuned for stable translation rather than open-ended generation:

Post-processing: decode only the generated suffix and take the first non-empty line. Looser decoding (sampling, longer windows, no repetition penalty) produces continuation artifacts and quote-tail noise.

Training

Procedure

The model was trained with a QLoRA supervised fine-tuning setup on a bidirectional Kashmiri ↔ English parallel corpus, on top of sarvamai/sarvam-translate. The resulting LoRA adapter was merged into the base weights; this release ships the merged checkpoint.

Hyperparameters

Observed training metrics

Training curves

Loss	Token accuracy	Eval loss	GPU memory

Raw training logs are available under artifacts/:

• stage1_training_log.jsonl
• stage1_summary.json
• stage1_eval.json
• stage1_metrics_summary.json

Evaluation

Internal eval (200 samples)

⚠️ Caveat. This score is implausibly perfect and almost certainly reflects an easy or in-distribution slice. It is reported here for completeness and must not be interpreted as a generalization estimate.

benchmark_simple_ks2en_200 (ks → en)

The benchmark contains some noisy or mismatched references; scores should be interpreted as a lower-bound indicator rather than a clean evaluation. Files: benchmarks/benchmark_simple_ks2en_200.{jsonl,stage1_eval.json}.

500-sample mixed-direction review set (source-only)

A 500-sample source-only set is provided for human review. Automatic BLEU/chrF is not applicable.

Hardware Requirements

Inference

Training (reference)

• Peak VRAM: 12.88 GB
• Wall-clock: 5.19 h for 20,060 steps

Repository Layout

.
├── README.md
├── config.json
├── generation_config.json
├── model.safetensors.index.json
├── model-0000{1..5}-of-00005.safetensors
├── tokenizer.json
├── tokenizer.model
├── artifacts/
│   ├── config.json
│   ├── stage1_eval.json
│   ├── stage1_metrics_summary.json
│   ├── stage1_summary.json
│   └── stage1_training_log.jsonl
├── assets/
│   ├── stage1_training_loss.svg
│   ├── stage1_token_accuracy.svg
│   ├── stage1_eval_loss.svg
│   └── stage1_gpu_alloc_gb.svg
├── benchmarks/
│   ├── benchmark_simple_ks2en_200.jsonl
│   ├── benchmark_simple_ks2en_200.stage1_eval.json
│   ├── kashmiri_benchmark_500_source_only.jsonl
│   ├── kashmiri_benchmark_500_stage1_safe_pred.jsonl
│   ├── kashmiri_benchmark_500_stage1_safe_pred.summary.json
│   ├── kashmiri_benchmark_500_stage1_safe_pred.xlsx
│   └── kashmiri_benchmark_500_stage1_safe_pred.summary.xlsx
└── notebooks/
    └── colab_load_stage1_model.ipynb

Limitations and Risks

• Decoding sensitivity. Output quality degrades sharply under sampling or loose repetition controls. Use the recommended deterministic settings.
• Length bias. The model was trained on sentence-level pairs (≤ 512 tokens). Long-form translation is not supported.
• Internal eval is not generalization. The 100/100 BLEU/chrF figure is a fixture, not a quality claim.
• Benchmark noise. Reported BLEU/chrF on benchmark_simple_ks2en_200 is depressed by reference noise; treat as indicative only.
• Domain coverage. The training distribution skews toward general / literary Kashmiri; performance on technical, legal, or dialectal inputs is unverified.
• Artifacts. Earlier decoding configurations occasionally produced quote-tail or continuation artifacts; the shipped generation_config.json mitigates but does not eliminate these.
• Low-resource caveat. Kashmiri remains a low-resource language; reference quality, orthographic normalization, and dialectal coverage are open problems that bound any model trained on currently-available data.

Citation

If you use this model, please cite:

@misc{malik2026koshurkouter,
  title        = {Koshur Kouter KS-EN v1: A Merged QLoRA Kashmiri--English Translation Model},
  author       = {Malik, Haq Nawaz and Nissar, Nahfid},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/Omarrran/koshur-kouter-ks-en_v1}},
  note         = {Fine-tuned from sarvamai/sarvam-translate}
}

Please also cite the base model:

@misc{sarvam2025translate,
  title        = {Sarvam-Translate},
  author       = {{Sarvam AI}},
  howpublished = {\url{https://huggingface.co/sarvamai/sarvam-translate}}
}

Acknowledgements

Built on top of sarvamai/sarvam-translate. Training and evaluation infrastructure run on Colab and Modal. Thanks to the broader Kashmiri NLP community whose data and tooling made this work possible.

Contact

• Hugging Face: @Omarrran · @nafiboi
• Issues / feedback: open a discussion on the model repository.