Instructions to use Faizaniqbal/KoshurAI_Tarjuma_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Faizaniqbal/KoshurAI_Tarjuma_v2 with Transformers:

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

pipe = pipeline("text-generation", model="Faizaniqbal/KoshurAI_Tarjuma_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Faizaniqbal/KoshurAI_Tarjuma_v2")
model = AutoModelForCausalLM.from_pretrained("Faizaniqbal/KoshurAI_Tarjuma_v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Faizaniqbal/KoshurAI_Tarjuma_v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Faizaniqbal/KoshurAI_Tarjuma_v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Faizaniqbal/KoshurAI_Tarjuma_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Faizaniqbal/KoshurAI_Tarjuma_v2

SGLang

How to use Faizaniqbal/KoshurAI_Tarjuma_v2 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 "Faizaniqbal/KoshurAI_Tarjuma_v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Faizaniqbal/KoshurAI_Tarjuma_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Faizaniqbal/KoshurAI_Tarjuma_v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Faizaniqbal/KoshurAI_Tarjuma_v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Faizaniqbal/KoshurAI_Tarjuma_v2 with Docker Model Runner:
```
docker model run hf.co/Faizaniqbal/KoshurAI_Tarjuma_v2
```

KoshurAI_Tarjuma_v2 — Kashmiri Continual Pretraining Base

⚠️ This is a base language model, not a translation model. For Kashmiri ↔ English translation, use the fine-tuned adapter: Faizaniqbal/KoshurAI_Tarjuma_v3

What is this?

`KoshurAI_Tarjuma_v2` is a 5B-parameter Gemma 3 model that has been continually pretrained on 2.8 million tokens of native Kashmiri text, giving it deep knowledge of the Kashmiri language (Perso-Arabic script). It serves as the Stage 1 base in the KoshurAI two-stage pipeline: `Omarrran/koshur-kouter-ks-en_v1 ← fine-tuned on Kashmiri–English pairs ↓ continual pretraining on 2.8M Kashmiri tokens Faizaniqbal/KoshurAI_Tarjuma_v2 ← this model (language knowledge) ↓ SFT on 16,637 EN↔KS pairs (LoRA adapter) Faizaniqbal/KoshurAI_Tarjuma_v3 ← final translation model`

Model Details

| | | |

|

| | Author | Faizan Iqbal ( @Faizaniqbal ) | | Base model | `Omarrran/koshur-kouter-ks-en_v1` | | Architecture | Gemma3ForCausalLM (5B parameters) | | Tensor type | BF16 | | Pretraining data | 2.8M tokens of Kashmiri text | | Languages | Kashmiri (ks · kas_Arab), English (en) | | License | Apache-2.0 |

Pretraining Corpus

The 2.8M token corpus was assembled from two sources: - InPage documents — professionally published Kashmiri literature, journalism, academic scholarship, and religious texts spanning multiple decades, converted to Unicode via a custom InPage converter. - Native speaker translations — texts translated into Kashmiri by native speakers, providing natural human-authored language coverage.

Full Model Lineage

google/gemma-3-4b-it       (Google)
    └─ sarvamai/sarvam-translate   (Sarvam AI)
           └─ Omarrran/koshur-kouter-ks-en_v1   (Malik & Nissar, 2026)
                  └─ Faizaniqbal/KoshurAI_Tarjuma_v2   ← this model
                         └─ Faizaniqbal/KoshurAI_Tarjuma_v3   (translation adapter)

Citation

@misc{iqbal2026koshurai,
  title        = {KoshurAI v3: A Fine-Tuned Neural Machine Translation System
                  for Kashmiri--English Bidirectional Translation},
  author       = {Iqbal, Faizan},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/Faizaniqbal/KoshurAI_Tarjuma_v3}}
}

Please also cite the base model:

@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}}
}