NoesisLab/NanoHammer-1.5B-Instruct
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NoesisLab
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NoesisLab advances machine learning research in deep contemplation and reflective reasoning to enable more profound and self-aware artificial intelligence.
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OzTianluย
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๐ NanoHammer-1.5B-Instruct:
NoesisLab/NanoHammer-1.5B-Instruct
We are excited to introduce NanoHammer, a novel architecture by NoesisLab designed for Causal State Compression and true Linear Inference Complexity.
๐ง The Core: Holographic State SpaceForget the growing KV Cache. NanoHammer leverages Holographic Rotary Embeddings to compress sequence history into a dynamic integral state.
Polynomial Compression: Instead of storing raw history, we "integrate" context into a complex number space , treating memory as a container of evolving polynomial coefficients.
Dynamic Evolution: The architecture features a custom StateUpdateCell that uses Euler method fixed-point iteration, allowing the model to perform implicit reasoning via differential state updates.
โก Why It Matters: Efficiency Meets Reasoning O(1) Inference Memory: State size remains constant regardless of sequence length.Causal Modeling: Explicitly models the causal flow of logic through time, perfect for "implicit reasoning" tasks without the verbosity of Chain-of-Thought.1.5B Lightweight Design: High performance, low resource footprint.
๐ Model Card HighlightsType: nanohammer (Hybrid Causal-State Architecture)
License: Apache 2.0
Capabilities: Instruction following, Long-context handling
๐ Try it on Hugging Face: NoesisLab/NanoHammer-1.5B-Instruct
NoesisLab/NanoHammer-1.5B-Instruct
We are excited to introduce NanoHammer, a novel architecture by NoesisLab designed for Causal State Compression and true Linear Inference Complexity.
๐ง The Core: Holographic State SpaceForget the growing KV Cache. NanoHammer leverages Holographic Rotary Embeddings to compress sequence history into a dynamic integral state.
Polynomial Compression: Instead of storing raw history, we "integrate" context into a complex number space , treating memory as a container of evolving polynomial coefficients.
Dynamic Evolution: The architecture features a custom StateUpdateCell that uses Euler method fixed-point iteration, allowing the model to perform implicit reasoning via differential state updates.
โก Why It Matters: Efficiency Meets Reasoning O(1) Inference Memory: State size remains constant regardless of sequence length.Causal Modeling: Explicitly models the causal flow of logic through time, perfect for "implicit reasoning" tasks without the verbosity of Chain-of-Thought.1.5B Lightweight Design: High performance, low resource footprint.
๐ Model Card HighlightsType: nanohammer (Hybrid Causal-State Architecture)
License: Apache 2.0
Capabilities: Instruction following, Long-context handling
๐ Try it on Hugging Face: NoesisLab/NanoHammer-1.5B-Instruct
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OzTianluย
published
a
model
3 days ago
OzTianluย
updated
a
Space
7 days ago
GGUF model please
1
#1 opened 9 days ago
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JLouisBiz
OzTianluย
updated
a
collection
13 days ago
Post
2790
Geilim-1B-SR-Instruct โ Serbian Intelligence for Deep Reasoning ๐ง ๐ท๐ธ
NoesisLab/Geilim-1B-SR-Instruct
Geilim-1B-SR-Instruct is a lightweight Large Language Model (LLM) designed to bring advanced reasoning capabilities to low-resource languages. It focuses on Serbian understanding and generation while maintaining robust English reasoning. Built on the LLaMA-3 architecture with a proprietary hybrid reasoning mechanism, it delivers deep logic while keeping outputs concise and natural. ๐
Core Innovations ๐ก
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. ๐ธ๏ธ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. โก
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. ๐
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. ๐ป
Use Cases ๐ ๏ธ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. ๐ฃ๏ธ
Educational Tools: Multi-turn interactive tasks and learning support. ๐
Key Advantages โจ
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. โ
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. ๐
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. ๐ค
NoesisLab/Geilim-1B-SR-Instruct
Geilim-1B-SR-Instruct is a lightweight Large Language Model (LLM) designed to bring advanced reasoning capabilities to low-resource languages. It focuses on Serbian understanding and generation while maintaining robust English reasoning. Built on the LLaMA-3 architecture with a proprietary hybrid reasoning mechanism, it delivers deep logic while keeping outputs concise and natural. ๐
Core Innovations ๐ก
Implicit Deep Reasoning: Combines standard attention mechanisms with graph-structured reasoning components for rigorous logic and causal inference. ๐ธ๏ธ
ASPP & -flow Hybrid Design: High-efficiency structured propagation + internal probability space optimization for high-quality reasoning without long-winded intermediate steps. โก
Bilingual Adaptation: Primarily focused on Serbian while preserving English logic, making it perfect for multilingual chats and cross-lingual tasks. ๐
Lightweight & Efficient: At ~1.3B parameters, it runs smoothly on consumer-grade GPUs, ideal for edge devices and research. ๐ป
Use Cases ๐ ๏ธ
Serbian Chatbots: Intelligent assistants with local linguistic nuance. ๐ฃ๏ธ
Educational Tools: Multi-turn interactive tasks and learning support. ๐
Key Advantages โจ
Clean Output: Avoids messy "thinking" tags; reasoning happens internally, delivering clear and direct results. โ
Open Access: Licensed under Apache-2.0, making it easy for research and engineering integration. ๐
AI Democratization: Empowering low-resource language ecosystems with cutting-edge intelligence. ๐ค
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- 1 reply
OzTianluย
updated
a
model
13 days ago
OzTianluย
published
a
model
13 days ago
OzTianluย
updated
a
model
13 days ago
OzTianluย
updated
a
model
14 days ago
Post
2563
๐ Geilim-1B-Instruct โ Implicit Deep Reasoning, Zero Verbosity
NoesisLab/Geilim-1B-Instruct
https://huggingface.co/collections/NoesisLab/geilim-large-language-models
No <think> tags. No long CoT.
Reasoning happens inside the hidden states, not in the output.
Whatโs different
๐ง Implicit reasoning: deep causal reasoning without exposing chains
๐ธ๏ธ ASPP (Adjacency-Structured Parallel Propagation): parent-only causal graph, O(n) message passing
๐ ฯ-flow: internal probability-space refinement instead of token-level deliberation
โ๏ธ Hybrid gating: learns when to use structure vs attention
Why it matters
Lower latency & token cost
Cleaner, production-ready outputs
CoT-level reasoning depth without verbosity tax
Built on Llama-3.2-1B-Instruct, trained for math, logic, and commonsense.
Designed for small-model reasoning at the edge.
#ImplicitReasoning #SmallLLM #EfficientAI #ReasoningModels #ASPP #PiFlow
NoesisLab/Geilim-1B-Instruct
https://huggingface.co/collections/NoesisLab/geilim-large-language-models
No <think> tags. No long CoT.
Reasoning happens inside the hidden states, not in the output.
Whatโs different
๐ง Implicit reasoning: deep causal reasoning without exposing chains
๐ธ๏ธ ASPP (Adjacency-Structured Parallel Propagation): parent-only causal graph, O(n) message passing
๐ ฯ-flow: internal probability-space refinement instead of token-level deliberation
โ๏ธ Hybrid gating: learns when to use structure vs attention
Why it matters
Lower latency & token cost
Cleaner, production-ready outputs
CoT-level reasoning depth without verbosity tax
Built on Llama-3.2-1B-Instruct, trained for math, logic, and commonsense.
Designed for small-model reasoning at the edge.
#ImplicitReasoning #SmallLLM #EfficientAI #ReasoningModels #ASPP #PiFlow
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- 2 replies
OzTianluย
updated
a
collection
16 days ago
OzTianluย
published
a
model
16 days ago
OzTianluย
updated
a
model
24 days ago
OzTianluย
updated
2
collections
24 days ago
OzTianluย
published
a
model
26 days ago
Post
1192
๐ Introducing Asterisk โ Hybrid ASPP-Attention Architecture! ๐
https://huggingface.co/NoesisLab/Asterisk
Weโre excited to launch Asterisk, a cutting-edge language model by NoesisLab on Hugging Face! ๐ Built on top of SmolLM2-135M-Instruct, Asterisk integrates Adjacency-Structured Parallel Propagation (ASPP) with standard attention to bring structured reasoning power into language modeling.
โจ Key Highlights:
๐น Hybrid Architecture โ Fuses graph-centric ASPP local reasoning with global attention for richer representations.
๐น Enhanced Reasoning โ ASPP enables iterative local state evolution that complements traditional transformer layers.
๐น Efficient Design โ ~171M parameters with smart supervised fine-tuning (Capybara dataset).
๐น Flexible & Open โ Apache-2.0 licensed and ready to integrate via Hugging Face ๐ค Transformers.
๐ Asterisk showcases how hybrid operators โ inspired by theoretical frameworks like the Asterisk Operator โ can bring structured reasoning into modern LMs in a scalable way.
๐ Try it out, explore the code, and start building: huggingface.co/NoesisLab/Asterisk
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