Instructions to use mohantvj/naadini-telugu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mohantvj/naadini-telugu with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir naadini-telugu mohantvj/naadini-telugu
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
เฐจเฐพเฐฆเฐฟเฐจเฐฟ (Naadini) - Telugu Creative AI
เฐจเฐพเฐฆเฐฟเฐจเฐฟ means "She who is the source of divine sound, poetry and wisdom"
Latest - V4 (Balanced Specialist)
V4 builds on V3's strong poetry foundation and adds much better songs and stories!
- Resumed training from V3 (poetry specialist)
- Added curated high-quality songs with proper pallavi/charanam structure
- Added curated stories with clear narrative and moral
- Kept poetry quality while broadening other skills
Version History & Comparison
| Feature | V1 | V2 | V3 | V4 (Current) |
|---|---|---|---|---|
| Base Model | Gemma 2B | Qwen3 14B | Qwen3 14B | Qwen3 14B |
| Val Loss | 2.60 | 0.493 | 0.208 | 0.678 |
| Training Focus | Mixed | Mixed | Poetry only | Poetry + Songs + Stories |
| Poetry Quality | Basic | Good | Excellent | Excellent |
| Songs | Poor | Good | Okay | Excellent |
| Stories | Poor | Okay | Okay | Much Better |
| Conversations | Good | Good | Good | Good |
| Overall Balance | Poor | Okay | Specialist | Best Balanced |
Note: Val loss for V4 is measured on a broader, mixed validation set (poetry + songs + stories + general), so it is not directly comparable to V3's poetry-only val loss. V4 is the most well-rounded version.
V4 is our best all-round model!
Capabilities
| Capability | Quality |
|---|---|
| Telugu Poems (เฐเฐตเฐฟเฐคเฐฒเฑ) | EXCELLENT |
| Classical Padyalu (เฐชเฐฆเฑเฐฏเฐพเฐฒเฑ) | EXCELLENT |
| Telugu Songs with proper structure | EXCELLENT |
| Devotional Songs (เฐญเฐเฑเฐคเฐฟ เฐชเฐพเฐเฐฒเฑ) | EXCELLENT |
| Folk Songs (เฐเฐพเฐจเฐชเฐฆ เฐชเฐพเฐเฐฒเฑ) | EXCELLENT |
| Telugu Stories with narrative | GOOD |
| Telugu Conversations | GOOD |
| Mother Songs (เฐ เฐฎเฑเฐฎ เฐชเฐพเฐเฐฒเฑ) | EXCELLENT |
Sample Outputs from V4
Folk Song (เฐเฐพเฐจเฐชเฐฆ เฐชเฐพเฐ)
เฐชเฐฒเฑเฐฒเฐตเฐฟ: เฐชเฐฒเฑเฐฒเฑ เฐเฑเฐตเฐฟเฐคเฐ เฐชเฑเฐฆเฑเฐฆ เฐชเฑเฐฐเฑเฐฎ, เฐจเฑเฐเฐฟ เฐฎเฑเฐฆ เฐเฑเฐฒเฑเฐฒเฑ เฐชเฐพเฐเฑ เฐเฐพเฐจเฐ. เฐชเฐฟเฐฒเฑเฐฒเฐฒ เฐจเฐตเฑเฐตเฑ เฐชเฐฒเฑเฐฒเฐตเฐฟ เฐฒเฐพเฐเฐเฐฟเฐฆเฐฟ, เฐฎเฐพเฐฎเฐฟเฐกเฐฟ เฐตเฑเฐเฑเฐทเฐ เฐเฑเฐเฑเฐเฑ เฐเฐพเฐฒเฐ.
เฐเฐฐเฐฃเฐ 1: เฐชเฐฒเฑเฐฒเฑ เฐเฑเฐกเฐฟ เฐเฑเฐกเฐพเฐฒเฐเฐเฑ เฐฎเฐจเฐธเฑเฐธเฑ เฐชเฐพเฐเฑ, เฐเฑเฐกเฐฟ เฐจเฐฟเฐฆเฑเฐฐ เฐเฑเฐเฐกเฑ เฐเฑเฐกเฐพ เฐชเฐพเฐเฑ.
Devotional (เฐญเฐเฑเฐคเฐฟ เฐชเฐพเฐ)
เฐชเฐฒเฑเฐฒเฐตเฐฟ: เฐตเฑเฐเฐเฐเฑเฐถเฑเฐตเฐฐเฐพ เฐจเฑ เฐชเฑเฐฐเฑ เฐตเฐฟเฐจเฑเฐจ เฐจเฐพเฐเฑ เฐเฐชเฐฟเฐฐเฐฟ เฐคเฑเฐธเฑเฐเฑเฐฒเฑเฐเฐชเฑเฐคเฑเฐเฐฆเฐฟ เฐจเฑ เฐญเฐเฑเฐคเฑเฐฒ เฐฎเฐจเฐธเฑเฐฒเฑ เฐจเฐฟเฐฒเฐฟเฐเฐฟเฐจ เฐจเฑ เฐชเฑเฐฐเฑ เฐ เฐฎเฑเฐคเฐฎเฑเฐตเฐฒเฑ เฐจเฐพ เฐฎเฐจเฐธเฑเฐจเฑ เฐจเฐฟเฐเฐชเฑเฐคเฑเฐเฐฆเฐฟ
Mother Song (เฐ เฐฎเฑเฐฎ เฐชเฐพเฐ)
เฐ เฐฎเฑเฐฎเฐพ เฐ เฐฎเฑเฐฎเฐพ เฐจเฑ เฐชเฑเฐฐเฑ เฐจเฐพ เฐเฑเฐตเฐฟเฐคเฐเฐฒเฑ เฐฎเฑเฐฒเฑเฐฒเฐเฐพ เฐจเฐพ เฐเฑเฐเฐกเฑเฐฒเฑ เฐจเฐฟเฐฒเฐฌเฐกเฐฟเฐเฐฆเฐฟ เฐจเฑ เฐเฑเฐชเฑเฐฒเฑเฐฒเฑ เฐชเฑเฐฐเฑเฐฎ เฐจเฐพ เฐเฑเฐตเฐฟเฐคเฐพเฐจเฑเฐจเฐฟ เฐฎเฐพเฐฐเฑเฐเฐฟเฐเฐฆเฐฟ
เฐเฑเฐฐเฐธเฑ: เฐ เฐฎเฑเฐฎเฐพ เฐจเฐฟเฐจเฑเฐจเฑ เฐจเฑเฐจเฑ เฐชเฑเฐฐเฑเฐฎเฐฟเฐธเฑเฐคเฐพเฐจเฑ เฐจเฑ เฐชเฑเฐฐเฑเฐฎ เฐจเฐพ เฐเฑเฐตเฐฟเฐคเฐ
Training Data
Real Classical Telugu Literature (Wikisource)
- เฐตเฑเฐฎเฐจ เฐชเฐฆเฑเฐฏเฐพเฐฒเฑ, เฐธเฑเฐฎเฐคเฐฟ เฐถเฐคเฐเฐ, เฐเฐพเฐณเฐนเฐธเฑเฐคเฑเฐถเฑเฐตเฐฐ เฐถเฐคเฐเฐ
- เฐ เฐจเฑเฐจเฐฎเฐพเฐเฐพเฐฐเฑเฐฏ เฐเฑเฐฐเฑเฐคเฐจเฐฒเฑ, เฐคเฑเฐฏเฐพเฐเฐฐเฐพเฐ เฐเฑเฐฐเฑเฐคเฐจเฐฒเฑ, เฐฐเฐพเฐฎเฐฆเฐพเฐธเฑ เฐเฑเฐฐเฑเฐคเฐจเฐฒเฑ
- เฐชเฑเฐคเฐจ เฐญเฐพเฐเฐตเฐคเฐ, เฐฎเฐจเฑเฐเฐฐเฐฟเฐคเฑเฐฐ, เฐเฐฎเฑเฐเฑเฐคเฐฎเฐพเฐฒเฑเฐฏเฐฆ
Curated Generated Data (V4 addition)
- 50 high-quality Telugu songs with proper structure
- 50 high-quality Telugu stories with narrative and moral
- 100 classical padyalu and 100 modern kavithalu
General Telugu Data
- Telugu instruction datasets sample
- Telugu conversations and instructions
Training Details
| Detail | Value |
|---|---|
| Base Model | Qwen3-14B (4-bit quantized) |
| Training Framework | MLX LoRA |
| Hardware | Apple M4 Mac Mini (24GB) |
| V4 Iterations | 3,000 (resumed from V3) |
| V4 Train Loss | 0.593 |
| V4 Val Loss | 0.678 (broader mixed validation set) |
| V4 Training Data | 4,323 curated examples |
| Num Layers | 16 |
| Learning Rate | 5e-6 |
| Batch Size | 2 |
How to Use
pip install mlx-lm
from mlx_lm import load, generate from mlx_lm.sample_utils import make_sampler
model, tokenizer = load( "mlx-community/Qwen3-14B-4bit", adapter_path="mohantvj/naadini-telugu" )
messages = [ { "role": "system", "content": "You are naadini, a Telugu creative writing assistant. Write in Telugu script. /no_think" }, { "role": "user", "content": "varsham gurinchi oka andhamaina kavita raayandi /no_think" } ]
text = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=False ) text = text + "
"sampler = make_sampler(temp=0.7, top_p=0.9, min_p=0.1) response = generate( model, tokenizer, prompt=text, max_tokens=400, sampler=sampler, verbose=True ) print(response)
Requirements
- Apple Silicon Mac (M1/M2/M3/M4)
- 16GB+ RAM recommended (24GB ideal)
- Python 3.9+
- mlx-lm library
Creator
Made with love by Thonangi Venkata Jagan Mohan
For Telugu community - telugu vaarandhariki ankitam
HuggingFace: https://huggingface.co/mohantvj
License
Apache 2.0 - Free to use, modify and build upon!
Acknowledgements
- MLX Community for Qwen3-14B-4bit
- Telugu Wikisource for classical literature
- AI4Bharat for Telugu datasets
- Apple for MLX framework
- Telugu community for downloads and support!
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