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Omarrran/
koshur-kouter-ks-en_v1

Name: koshur-kouter-ks-en_v1
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Koshur Kouter KS-EN v1

Open In Colab

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


Authors	Haq Nawaz Malik (@Omarrran) · Nahfid Nissar (@nafiboi)
Base model	`sarvamai/sarvam-translate`
Architecture	`Gemma3ForCausalLM`
Parameters	~4.55 B (bf16)
Languages	Kashmiri (ks), English (en)
License	GPL-3.0
Precision	bfloat16
Context length	131,072 tokens
Vocabulary size	262,208
Layers / hidden / heads / KV heads	34 / 2560 / 8 / 4
Checkpoint size	8.48 GiB (5 shards)

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

python

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

python

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.

Open In Colab

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:

Parameter	Value
`do_sample`	`False`
`max_new_tokens`	`48`
`repetition_penalty`	`1.15`
`no_repeat_ngram_size`	`3`

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

Hyperparameter	Value
Max sequence length	512
Effective batch size	32
Gradient accumulation	1
Precision	bf16
Optimizer	`paged_adamw_8bit`
Learning rate	2 × 10⁻⁴
LR scheduler	cosine
Warmup ratio	0.03
Weight decay	0.01
Max grad norm	1.0
Epochs	2
LoRA rank / alpha / dropout	32 / 64 / 0.05
LoRA target modules	`q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj`
Modules saved (full)	`lm_head, embed_tokens`
Logging / eval / save steps	10 / 250 / 250

Observed training metrics

Metric	Value
Total steps	20,060
Wall-clock time	5.19 h
Peak VRAM	12.88 GB
Train loss — first logged	8.0698
Train loss — best logged	0.5632
Train loss — last step (step 20,060)	0.5819
Final train loss (averaged across run)	0.6242
Token accuracy — last / best	0.8548 / 0.8557
Eval loss — first / best	1.4610 / 1.2011
Final eval loss (step 20,000)	1.2294

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)

Metric	Value
BLEU	100.0
chrF	100.0

⚠️ 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)

Metric	Value
BLEU	13.07
chrF	46.99
Exact match	0.00

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.

File	Purpose
`kashmiri_benchmark_500_source_only.jsonl`	Source prompts
`kashmiri_benchmark_500_stage1_safe_pred.jsonl`	Model predictions
`kashmiri_benchmark_500_stage1_safe_pred.{summary.json, xlsx, summary.xlsx}`	Aggregates and review-friendly spreadsheets

Hardware Requirements

Inference

Setting	VRAM	Notes
Short bf16 inference (observed)	~8.5 GB	Validated on Modal
Recommended for short prompts	≥ 12 GB	Comfortable headroom
Long prompts / batching	16–24 GB+	Recommended
CPU loading	≥ 24 GB RAM	Full merged shards held in memory
Colab	L4 / A100	Use 4/8-bit quantization on smaller GPUs

Training (reference)

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

Repository Layout

text

.
├── 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:

bibtex

@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: