---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: mlx
tags:
  - mlx
  - lora
  - translation
  - mewari
  - rajasthani
  - hindi
  - en-mwr
  - fine-tuned
  - apple-silicon
language:
  - en
  - hi
license: apache-2.0
pipeline_tag: text-generation
---

# Qwen2.5-7B Mewari Translation (MLX LoRA)

A LoRA fine-tuned adapter for **English to Mewari translation**, built on top of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) using [MLX](https://github.com/ml-explore/mlx) on Apple Silicon.

**Mewari** (मेवाड़ी) is a Rajasthani language spoken in the Mewar region of Rajasthan, India, written in Devanagari script.

## Usage

### With MLX (Apple Silicon)

```python
from huggingface_hub import snapshot_download
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler

# Download adapter from HuggingFace
adapter_path = snapshot_download(repo_id="viplismism/Qwen2.5-7B-Mewari-MLX-LoRA")

# Load base model with adapter
model, tokenizer = load("Qwen/Qwen2.5-7B-Instruct", adapter_path=adapter_path)

messages = [
    {"role": "system", "content": "You are an expert translator specializing in English to Mewari translation. Provide only the direct Mewari translation in Devanagari script, nothing else."},
    {"role": "user", "content": 'English text to translate: "Hello, how are you?"\n\nProvide the Mewari translation:'},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
sampler = make_sampler(temp=0.7, top_p=0.9)
response = generate(model, tokenizer, prompt=prompt, max_tokens=256, sampler=sampler)
print(response)
# Output: नमस्ते, थूं कैसै है?
```

## Example Translations

| English | Mewari |
|---------|--------|
| Hello, how are you? | नमस्ते, थूं कैसै है? |
| The weather is very hot today | आज मौसम घणो गरम है। |
| Please sit down and have some tea | कृपया बैठ जावो अर कुछ चाय खावो। |
| What is your name? | थारो नाव क्या है? |
| Where are you going tomorrow? | काल थें कठै जावां? |
| My children go to school every day | म्हारै बाचेरे हर दिन स्कूल जावै है। |

## Training Details

| Parameter | Value |
|-----------|-------|
| Base Model | Qwen/Qwen2.5-7B-Instruct |
| Method | LoRA (MLX) |
| Training Data | 2,700 English-Mewari pairs |
| Validation Data | 300 English-Mewari pairs |
| LoRA Rank | 64 |
| LoRA Alpha | 128 |
| LoRA Dropout | 0.1 |
| Learning Rate | 1e-5 |
| Batch Size | 1 |
| Iterations | 1000 |
| Max Seq Length | 512 |
| Grad Checkpoint | Yes |

### Training Results

| Metric | Value |
|--------|-------|
| Train Loss | 2.076 → 0.265 |
| Val Loss | 2.703 → 0.290 |
| Best Val Loss | 0.282 (iter 700) |
| Peak Memory | 18.856 GB |
| Hardware | Apple M4 Max (36GB) |

## Limitations

- Optimized for simple to moderate sentence translation
- May produce repetition on certain complex or compound sentences
- Best used with temperature 0.7 and top_p 0.9
- MLX adapter format — designed for Apple Silicon inference