**First community NVFP4 quantization** of google/gemma-4-26B-A4B-it — the Mixture-of-Experts variant of Gemma 4 with 25.2B total parameters and only 3.8B active per token. Tested on NVIDIA DGX Spark (128GB unified memory, GB10 Blackwell, SM 12.1). Model loads at 15.7 GiB — plenty of headroom for 65K+ context with FP8 KV cache. Gemma 4 MoE stores expert weights as **fused 3D tensors** (`nn.Parameter` of shape `[128, dim, dim]`) instead of individual `nn.Linear` modules. NVIDIA Model Optimizer (modelopt) only quantizes `nn.Linear` — it silently skips the 3D expert parameters, which are 91% of the model.
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View details ↗* Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗Bert is a Transformer Bidirectional Encoder based Architecture trained on MLM(Mask Language Modeling) objective bert-base-uncased finetuned on the emotion dataset using HuggingFace Trainer with below training parameters ```bibtex @misc{savani2021bertemotion, author = {Savani, Bhadresh}, title = {{bert-base-uncased-emotion: BERT Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion} }
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View details ↗l6RfqcHPlI0jWr7TVGoFsWZ64YAg - type: recall value: 0.927 name: Recall Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjEyYmZiZDQ4MzM1ZmQ2ZmJhZWU4OTVkNmViYjA5NzhiN2MxODE0MzUxZTliZTk0MzViZDAyNGU4MDFjYjM1MSIsInZlcnNpb24iOjF9.9lazxLXbPOdwhqoYtIudwRwjfNVZnUu7KvGRklRP_RAoQStAzgmWMIrT3ckX_d5_6bKZH9fIdujUn5Qz-baKBw - type: recall value: 0.927 name: Recall Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMWVhMzY0YTA4YmQzYTg4YTBiMzQ5YzRiZWJhMjM1NjUzZGQxZmQ5M2NkZDcyNTQ0ZmJjN2NkY2ZiYjg0OWI0ZCIsInZlcnNpb24iOjF9.QgTv726WCTyvrEct0NM8Zpc3vUnDbIwCor9EH941-zpJtuWr-xpdZzYZFJfILkVA0UUn1y6Jz_ABfkfBeyZTBg - type: f1 value: 0.8825061528287809 name: F1 Macro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzQzZTJkMDAwOTUwMzY3ZjI2MjIxYjlmZTg3YTdhNTc4ZjYyMmQ2NDQzM2FmYzk3OGEzNjhhMTk3NTQ3OTlhNyIsInZlcnNpb24iOjF9.hSln1KfKm0plK7Qao9vlubFtAl1M7_UYHNM6La9gEZlW_apnU1Mybz03GT2XZORgOVPe9JmgygvZByxQhpsYBw - type: f1 value: 0.927 name: F1 Micro verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiNzljODQ3NjE3MDRkODE3ZjFlZmY5MjYyOGJlNDQ4YzdlZGRiMTI5OGZiZWM2ODkyZjMyZWQ3MTkzYWU5YThkOCIsInZlcnNpb24iOjF9.7qfBw39fv22jSIJoY71DkOVr9eBB-srhqSi09bCcUC7Huok4O2Z_vB7gO_Rahh9sFgKVu1ZATusjTmOLQr0fBw - type: f1 value: 0.926876082854655 name: F1 Weighted verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMjJhN2UzODgxOWQ0Y2E3YTcwZTQxMDE0ZWRmYThjOWVhYWQ1YjBhMzk0YWUxNzE2ZjFhNWM5ZmE2ZmI1YTczYSIsInZlcnNpb24iOjF9.nZW0dBdLmh_FgNw6GaITvSJFX-2C_Iku3NanU8Rip7FSiRHozKPAjothdQh9MWQnq158ZZGPPVIjtyIvuTSqCw - type: loss value: 0.17403268814086914 name: loss verified: true verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTVjZmFiOGQwZGY1OTU5YWFkNGZjMTlhOGI4NjE3MGI4ZDhkODcxYmJiYTQ3NWNmMWM0ODUyZDI1MThkYTY3ZSIsInZlcnNpb24iOjF9.OYz5BI3Lz8LgjAqVnD6NcrG3UAG0D3wjKJ7G5298RRGaNpb621ycisG_7UYiWixY7e2RJafkfRiplmkdczIFDQ --- ```bibtex @misc{savani2021distilbertemotion, author = {Savani, Bhadresh}, title = {{distilbert-base-uncased-emotion: DistilBERT Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion} } * Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗```bibtex @misc{savani2021robertaemotion, author = {Savani, Bhadresh}, title = {{roberta-base-emotion: RoBERTa Fine-tuned for Emotion Classification}}, year = {2021}, publisher = {Hugging Face}, doi = {10.57967/hf/XXXX}, url = {https://huggingface.co/bhadresh-savani/roberta-base-emotion} } * Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf
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View details ↗**BharatGen** presents **Param-2-17B-MoE-A2.4B**, a large-scale Mixture-of-Experts (MoE) language model designed to deliver high model capacity while retaining the inference efficiency of a much smaller dense model. It uses a **Hybrid MoE architecture** with **17B total parameters**, while activating only **2.4B parameters per token**. The model is pretrained from scratch, with a strong emphasis on **linguistic diversity**, **cultural grounding**, and **multilingual representation**, particularly for Indian languages. It is released as an **early post-training checkpoint** with advanced capabilities including reasoning, tool calling, mathematics, and code generation, making it suitable for diverse downstream applications and further fine-tuning. This broad language coverage enables stronger performance on **region-specific tasks** and promotes **inclusive representation** of India’s diverse and underrepresented linguistic communities.
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