Hari5115/MoleculeIQ-SmolLM2-360M
Text Generation • 0.4B • Updated
Hariprasad Sundaresan PRO
Hari5115
AI & ML interests
LLMs, Fine-tuning, Agentic AI, RAG, Multilingual NLP, Transformers
Recent Activity
reacted to PhysiQuanty's post with 🔥 about 11 hours ago
🧠 Arithmetic-SLM : A 30M model that manages to compute simple arithmetic better than a 3B model 🚀
https://huggingface.co/WhirlwindAI/Arithmetic-SLM
https://huggingface.co/spaces/WhirlwindAI/arithmetic-slm
🏆 Leaderboard ArithMark-2 🏆
🥇 Qwen/Qwen2.5-Math-1.5B = 82.08%
🥈 WhirlwindAI/Arithmetic-SLM = 78.60% (31.7M Params)
🥉 Qwen/Qwen2.5-3B = 78.44%
Example WhirlwindAI/Arithmetic-SLM =
0.5 * 0.5 = 0.25 ✅
105 + 45 / 8 = 110 ✅
(132 / 12) + (46 - 15) = 42 ✅
(10 + 28) * 3 = 114 ✅
1 * (16 + 28) = 44 ✅
(21 + 27) * (14 - 7) = 336 ❌
```python
leaderboard = """
| Model | Params | Score |
|----------------------------------|--------------|-----------|
| Qwen/Qwen2.5-Math-1.5B | 1.54B | 82.08% |
| WhirlwindAI/Arithmetic-SLM | 31.70M | 78.60% | <=
| Qwen/Qwen2.5-3B | 3.09B | 78.44% |
| Qwen/Qwen2.5-1.5B | 1.54B | 77.72% |
| Qwen/Qwen2.5-Coder-1.5B | 1.54B | 74.88% |
| HuggingFaceTB/SmolLM2-1.7B | 1.71B | 66.12% |
| Qwen/Qwen2.5-0.5B | 494M | 63.04% |
| facebook/MobileLLM-R1-140M-base | 140M | 53.88% |
| SupraLabs/Supra-50M-Base | 52M | 27.12% |
"""
```
Bench =
https://huggingface.co/datasets/AxiomicLabs/ArithMark-2.0
DataSet =
https://huggingface.co/datasets/WhirlwindAI/Arithmetic
By Science AND FOR SCIENCE <3 reacted to pranavupadhyaya52's post with 👍 about 11 hours ago
After several weeks of experimenting, debugging, and iterating, I am excited to share WikiSmartBotLM.
WikiSmartBotLM is a compact decoder only language model built from the ground up as an educational and practical project. The goal was not simply to train another language model, but to create one that is easy to understand, modify, and experiment with while following many of the architectural ideas used in modern LLMs.
The model is built on a custom Transformer architecture featuring Rotary Positional Embeddings, RMSNorm, SwiGLU feed forward layers, grouped query attention, and an efficient autoregressive decoder optimized for local inference.
The repository includes the complete model implementation, configuration files, tokenizer integration, training pipeline, inference scripts, checkpoint conversion utilities, and examples that demonstrate how each component works together. Whether you want to understand the forward pass, train your own model, or build applications on top of WikiSmartBotLM, everything is designed to be approachable.
You can directly run the model via the models Huggingfaces space, which I've included in the post.
Model Repository: https://huggingface.co/pranavupadhyaya52/Wiki-SmartBotLM-Instruct
I hope WikiSmartBotLM becomes a useful resource for anyone who enjoys learning by building. Feedback, issues, feature requests, and contributions are always welcome.
https://huggingface.co/spaces/pranavupadhyaya52/WikiSmartBot reacted to SeaWolf-AI's post with 👍 about 11 hours ago
🔓 We ran genuine quantum key-recovery on 'real IBM quantum hardware' — and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).
Using Simon's algorithm on `ibm_kingston`, we recovered the secret key of two symmetric-cipher structures:
• Even–Mansour — N=5 → N=10
• 3-round Feistel (DES-family) — block 6 → 8
Each verified against an 'independent control key', using error mitigation only (no QEC).
🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.
📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
🏆 Leaderboard: https://huggingface.co/spaces/FINAL-Bench/quantum-bench-leaderboard
#quantum #cryptography #quantumcomputingOrganizations
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