---
base_model: qwen3
language:
  - si
  - en
pipeline_tag: text-generation
library_name: transformers
license: apache-2.0
datasets:
  - your_dataset_name_here
tags:
  - unsloth
  - qwen
  - qwen3
  - sinhala
  - text-generation
  - custom-finetune
  - causal-lm
  - nlp
  - pytorch
---

# Sinhala Qwen 3 - Fine-Tuned Model (v7500)

## Model Description
This is a fine-tuned version of the **Qwen 3** base model, specifically trained to understand and generate the **Sinhala** language. 

Originally, the base model had absolutely no understanding of Sinhala. Through custom fine-tuning using the [Unsloth](https://github.com/unslothai/unsloth) library, this model has been taught the foundational elements of the language from scratch. It is now capable of basic Sinhala comprehension and text generation, marking a significant step in low-resource language adaptation for this architecture.

## Model Details
* **Base Model:** Qwen 3 (`Qwen3ForCausalLM`)
* **Language(s):** Sinhala (si), English (en)
* **Fine-Tuning Library:** Unsloth (`unsloth_version: 2026.1.4`)
* **Checkpoint/Version:** v7500 (Trained up to 7500 steps)
* **Status:** Experimental / Early Stage

## Intended Use
This model is intended for researchers and developers working on Natural Language Processing (NLP) for the Sinhala language. It can be used as a starting point for further fine-tuning, vocabulary expansion, or basic Sinhala text generation tasks.

*Note: Since the model was trained from scratch to learn a completely new language, it might still make grammatical errors or occasionally struggle with complex sentence structures. It represents a foundation that is continuously being improved.*

## How to Use

You can use this model directly with the `transformers` library in Python:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer


model_id = "sh4lu-z/Sinhala-Qwen3-v7500" 

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

prompt = "ඔබට කොහොමද?"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Procedure

This model was trained using the Unsloth library to significantly speed up the fine-tuning process while maintaining accuracy.

#### `Framework:` PyTorch / Transformers

#### `Hardware:` Fine-tuned on GPU instances