Instructions to use dhanesh-hf/Jarvis-Titan-M4-Activated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dhanesh-hf/Jarvis-Titan-M4-Activated with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dhanesh-hf/Jarvis-Titan-M4-Activated", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("dhanesh-hf/Jarvis-Titan-M4-Activated", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dhanesh-hf/Jarvis-Titan-M4-Activated with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dhanesh-hf/Jarvis-Titan-M4-Activated"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dhanesh-hf/Jarvis-Titan-M4-Activated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dhanesh-hf/Jarvis-Titan-M4-Activated

SGLang

How to use dhanesh-hf/Jarvis-Titan-M4-Activated with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dhanesh-hf/Jarvis-Titan-M4-Activated" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dhanesh-hf/Jarvis-Titan-M4-Activated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dhanesh-hf/Jarvis-Titan-M4-Activated" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dhanesh-hf/Jarvis-Titan-M4-Activated",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dhanesh-hf/Jarvis-Titan-M4-Activated with Docker Model Runner:
```
docker model run hf.co/dhanesh-hf/Jarvis-Titan-M4-Activated
```

Jarvis-Titan-M4-Activated / 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}}
	}
	```