| --- |
| license: apache-2.0 |
| language: |
| - en |
| - hi |
| - mr |
| - ta |
| - te |
| - kn |
| - ml |
| - bn |
| - pa |
| - gu |
| - or |
| pipeline_tag: text-generation |
| library_name: transformers |
| base_model: |
| - FrontiersMind/Nandi-Mini-150M |
| --- |
| |
| # Nandi-Mini-150M-Instruct |
|
|
| ## Introduction |
|
|
| Nandi-Mini-150M-Instruct is a compact, efficient multilingual language model designed for strong performance in resource-constrained environments. It is pre-trained from scratch on 525 billion tokens and further enhanced through instruction tuning and Direct Preference Optimization (DPO). The model supports English and 10 Indic languages. |
|
|
|
|
| Nandi-Mini-150M-Instruct focuses on maximizing performance per parameter through architectural efficiency rather than scale. It is optimized for edge devices, on-prem deployments, and low-latency applications, making it ideal for resource-constrained environments. |
| Nandi-Mini-150M-Instruct brings the following key features: |
|
|
| - Strong **multilingual capability** across English and Indic languages |
| - Efficient design enabling **high performance at small scale (150M parameters)** |
| - Reduced memory footprint using **factorized embeddings** |
| - Better parameter efficiency through **layer sharing** |
|
|
| ## 📝 Upcoming Releases & Roadmap |
|
|
| We’re just getting started with the Nandi series 🚀 |
|
|
| - **Nandi-Mini-150M-Tool-Calling (Specialized-Model)** — Coming Soon this week |
| - **Nandi-Mini-500M (Base + Instruct)** — Pre-Training Going On |
| - **Nandi-Mini-1B (Base + Instruct)** — Pre-Training Going On |
|
|
|
|
| 📢 **Blogs & technical deep-dives coming soon**, where we’ll share: |
| - Architecture decisions and design trade-offs |
| - Training insights and dataset composition |
| - Benchmarks and real-world applications |
|
|
| Stay tuned! |
|
|
|
|
| ## 🌍 Supported Languages |
|
|
| The model is trained on English and a diverse set of Indic languages, including: |
|
|
| - Hindi, Bengali, Tamil, Telugu, Marathi, Gujarati, Kannada, Malayalam, Punjabi, Odia |
|
|
|
|
|
|
| ## 🚀 Usage |
|
|
| ```python |
| !pip install transformers=='5.4.0' |
| |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| |
| model_name = "FrontiersMind/Nandi-Mini-150M-Instruct" |
| |
| device = "cuda" if torch.cuda.is_available() else "cpu" |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| trust_remote_code=True, |
| dtype=torch.bfloat16 |
| ).to(device).eval() |
| |
| prompt = "Explain newton's second law of motion" |
| |
| messages = [ |
| {"role": "user", "content": prompt} |
| ] |
| |
| prompt = tokenizer.apply_chat_template(messages, tokenize=False) |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| |
| generated_ids = model.generate( |
| **inputs, |
| max_new_tokens=500, |
| do_sample=True, |
| temperature=0.3, |
| top_p=0.90, |
| top_k=20, |
| repetition_penalty=1.1, |
| ) |
| |
| generated_ids = [ |
| output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, generated_ids) |
| ] |
| |
| response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
| |
| print(response) |
| ``` |
|
|
|
|
|
|
| ## 📬 Feedback & Suggestions |
|
|
| We’d love to hear your thoughts, feedback, and ideas! |
|
|
| - **Discord**: https://discord.gg/ZGdjCdRt |
| - **Email:** support@frontiersmind.ai |
| - **Official Website** https://www.frontiersmind.ai/ |
| - **LinkedIn:** https://www.linkedin.com/company/frontiersmind/ |
| - **X (Twitter):** https://x.com/FrontiersMind |
