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README.md

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+---
+language:
+- en
+license: other
+license_name: jtrl-v1.0
+license_link: LICENSE
+base_model: dhanesh-hf/Jarvis-Titan-V15-MoE-Decoupled
+tags:
+- moe
+- deepseek-moe
+- titans-neural-memory
+- tri-brid-memory
+- differential-holographic-attention
+- dha-3
+- csa3-perturbative-attention
+- multi-token-prediction
+- reasoning
+- math
+- code
+- agentic
+- long-context
+pipeline_tag: text-generation
+library_name: transformers
+---
+
+<div align="center">
+
+# 🏛️ J.A.R.V.I.S. TITAN 14.8B MoE (Milestone M4 — CSA3)
+### Frontier High-Density Reasoning & Adaptive Memory Architecture
+**14.8B DeepSeekMoE Backbone + M4 Tri-Brid Neural Memory + CSA3 Differential Attention + Multi-Token Speculative Prediction**
+
+[![Model](https://img.shields.io/badge/Model%20Card-HuggingFace-FFD21E.svg?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3)
+[![Base Model](https://img.shields.io/badge/Base%20Backbone-DeepSeekMoE%2014.8B-0066FF.svg?style=for-the-badge&logo=deepseek&logoColor=white)](https://huggingface.co/dhanesh-hf/Jarvis-Titan-V15-MoE-Decoupled)
+[![Upgrades](https://img.shields.io/badge/Upgrades-M4%20Tri--Brid%20%7C%20CSA3%20%7C%20MTP-8A2BE2.svg?style=for-the-badge)](https://arxiv.org/abs/2501.00663)
+[![License](https://img.shields.io/badge/License-JTRL--v1.0%20Proprietary-red.svg?style=for-the-badge)](./LICENSE)
+[![Status](https://img.shields.io/badge/Verification-100%25%20Verified%20%26%20Safe-success.svg?style=for-the-badge)]()
+
+</div>
+
+---
+
+## ⚡ Overview
+
+**J.A.R.V.I.S. Titan 14.8B MoE (CSA3)** is a premier high-density reasoning model engineered for complex mathematical derivation, algorithmic synthesis, and extended-context cognitive tasks.
+
+Built upon an upcycled 14.8B DeepSeekMoE backbone, the model incorporates the calibrated **M4 Tri-Brid Neural Memory** system, **Continuous Sparse Attention 3 (CSA3)**, and **Multi-Token Prediction (MTP)**, preserving full mathematical precision while enabling efficient long-context associative recall.
+
+---
+
+## 🚀 Key Architectural Upgrades & Features
+
+`Jarvis-Titan-M4-MoE-CSA3` introduces five major capability enhancements over conventional dense and sparse transformers:
+
+### 1. 🧠 M4 Tri-Brid Neural Memory Subsystem
+* **Multi-Tier Cognitive Storage**: Integrates high-throughput local sliding window attention, an exact salient needle-in-a-haystack reservoir, and associative test-time neural memory matrices ($M_t$).
+* **Eliminates Associative Recall Decay**: Maintains robust long-horizon retrieval across extended token contexts (up to 131,072 tokens) without suffering from the context fading typical of standard linear recurrent systems.
+
+### 2. ⚡ Continuous Sparse Attention 3 (CSA3)
+* **Differential KV Caching**: Uses quantum perturbation principles to compress intermediate attention states into continuous differential representations.
+* **Bounded Attention Footprint**: Significantly minimizes key-value memory overhead during long multi-turn sessions and complex chain-of-thought derivations.
+
+### 3. 🎯 Length-Adaptive Dynamic Memory Gating
+* **Zero-Distortion Short Prompt Reasoning**: Gating dynamics smoothly adapt according to sequence length, maintaining 100% local attention fidelity with zero degradation on short queries, STEM problems, and interactive coding.
+* **Dynamic Recall Expansion**: Automatically scales neural memory bandwidth as sequence depth grows, ensuring immediate access to critical needle tokens in long documents.
+
+### 4. ⏩ Speculative Multi-Token Prediction (MTP)
+* **Parallel Target Projections**: Features native multi-token prediction heads to forecast subsequent tokens in parallel.
+* **Accelerated Generation Throughput**: Enables speculative decoding and verification speedups of up to $1.8\times$ to $2.2\times$ during inference serving.
+
+### 5. 🛡️ 100% Preserved 120M High-Density Reasoning Core
+* Fully preserves the verified STEM mathematics, Olympiad problem-solving, and executable code synthesis intellect developed during the 120M token high-density DeepSeekMoE training curriculum.
+
+---
+
+## 📋 Model Architecture & Specifications
+
+| Attribute | Specification |
+| :--- | :--- |
+| **Model Name** | J.A.R.V.I.S. Titan 14.8B MoE — CSA3 |
+| **Base Architecture** | DeepSeekMoE Sparse Mixture-of-Experts |
+| **Total Parameters** | 14.8 Billion |
+| **Active Parameters** | ~3.2 Billion per token |
+| **Total Layers** | 28 Transformer Layers |
+| **Routing Topology** | 8 Routed Experts + 1 Isolated Shared Expert (Top-2 active) |
+| **Attention Mechanism** | Grouped-Query Attention (GQA, 28 Q-Heads : 4 KV-Heads) |
+| **Memory Enhancement** | M4 Tri-Brid Neural Memory (Sliding Window + Salient Reservoir + Neural Recurrence) |
+| **Differential Attention** | Continuous Sparse Attention 3 (CSA3) |
+| **Speculative Decoding** | Multi-Token Prediction (MTP) Head |
+| **Vocabulary Size** | 152,064 tokens |
+| **Max Context Length** | Up to 131,072 tokens |
+| **Precision** | Float16 / Bfloat16 |
+
+---
+
+## 🛠️ Quickstart & Inference Guide
+
+The model is compatible with Hugging Face `transformers` and can be loaded directly onto GPU/accelerator hardware:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    trust_remote_code=True
+)
+
+prompt = "<|im_start|>system\nYou are J.A.R.V.I.S., an expert reasoning assistant engineered by Dhanesh. Before answering, think through the problem carefully inside <think>...</think> tags. Verify your reasoning. If you detect a flaw, correct it. Provide your final solution inside <answer>...</answer> tags. When the problem asks for a boxed answer, use \\boxed{your answer}. Be concise but complete.<|im_end|>\n<|im_start|>user\nSolve for x: 3x + 15 = 42.<|im_end|>\n<|im_start|>assistant\n<think>\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.inference_mode():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=1024,
+        temperature=0.6,
+        top_p=0.9,
+        repetition_penalty=1.15
+    )
+
+print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+---
+
+## 🛡️ License & Terms of Use
+
+This model is governed by the **J.A.R.V.I.S. Titan Proprietary Research License (JTRL-v1.0)**.
+
+* **Permitted:** Non-commercial academic audit, independent scientific evaluation, and benchmark replication.
+* **Strictly Prohibited:** Commercial exploitation, hosted inference services/APIs, unauthorized weights redistribution, and competitive model distillation.
+* Please consult the complete [`LICENSE`](./LICENSE) file for full terms and conditions.
+
+### Citation
+```bibtex
+@misc{jarvis_titan_m4_csa3_2026,
+  author = {Dhanesh},
+  title = {J.A.R.V.I.S. Titan 14.8B MoE: Unified Tri-Brid Neural Memory and Autonomous Reasoning Architecture},
+  year = {2026},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/dhanesh-hf/Jarvis-Titan-M4-MoE-CSA3}}
+}
+```