Instructions to use vinguyen0891/vivy-gemma-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use vinguyen0891/vivy-gemma-e4b with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf vinguyen0891/vivy-gemma-e4b # Run inference directly in the terminal: llama cli -hf vinguyen0891/vivy-gemma-e4b
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf vinguyen0891/vivy-gemma-e4b # Run inference directly in the terminal: llama cli -hf vinguyen0891/vivy-gemma-e4b
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf vinguyen0891/vivy-gemma-e4b # Run inference directly in the terminal: ./llama-cli -hf vinguyen0891/vivy-gemma-e4b
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf vinguyen0891/vivy-gemma-e4b # Run inference directly in the terminal: ./build/bin/llama-cli -hf vinguyen0891/vivy-gemma-e4b
Use Docker
docker model run hf.co/vinguyen0891/vivy-gemma-e4b
- LM Studio
- Jan
- Ollama
How to use vinguyen0891/vivy-gemma-e4b with Ollama:
ollama run hf.co/vinguyen0891/vivy-gemma-e4b
- Unsloth Studio
How to use vinguyen0891/vivy-gemma-e4b with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vinguyen0891/vivy-gemma-e4b to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for vinguyen0891/vivy-gemma-e4b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for vinguyen0891/vivy-gemma-e4b to start chatting
- Docker Model Runner
How to use vinguyen0891/vivy-gemma-e4b with Docker Model Runner:
docker model run hf.co/vinguyen0891/vivy-gemma-e4b
- Lemonade
How to use vinguyen0891/vivy-gemma-e4b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull vinguyen0891/vivy-gemma-e4b
Run and chat with the model
lemonade run user.vivy-gemma-e4b-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
- VIVY — Technical Model Card
- Model Overview
- Architecture: N-Thought Principal Scientist Core (NPS Core)
- 4-Level Filter Funnel (vivy:latest)
- Unitary Reasoner Protocol (vivy:unitary)
- Model Variants
- Benchmark Results (Local CPU)
- N-Thought Population Output Format
- Language
- Hard Rules
- Technical Specifications
- Provenance
- Citation
- Intended Use
- Limitations
- License
- Model Overview
VIVY — Technical Model Card
Model Overview
| Field | Value |
|---|---|
| Name | Vivy (ViVy) |
| Version | V1.0 (latest), V0.2.0 (unitary) |
| Developer | Ngoc Chau · 199X Ngọc Châu |
| Project | NPS Core V1 — ADR-0021 |
| Base Model | Gemma 4 E4B (Google) |
| Quantization | Q4_K_M (9.6 GB GGUF) |
| License | Apache 2.0 (inherited from Gemma 4) |
| Architecture | Decoder-only Transformer, 4B parameters |
| Context Length | 2048 tokens (configurable) |
| Thinking Mode | Native (Gemma 4 built-in) |
Architecture: N-Thought Principal Scientist Core (NPS Core)
NPS Core is not a fine-tuned model. Vivy is a base model + structured system prompt that implements the NPS Core V1 architecture — a Principal Scientist Model that maintains a population of hypotheses rather than a single chain-of-thought.
Six Core Mechanisms
1. Population Reasoning
Vivy maintains N_h=4 parallel hypotheses as structured state objects, not free-text sequences. Each hypothesis carries:
- Content (the hypothesis itself)
- Confidence score (0.0–1.0)
- Stream classification:
[P]Premise,[C]Conclusion,[I]Interference
2. Delegated Cognition
Vivy distinguishes between:
- Tasks to reason about itself
- Tasks to plan
- Tasks to delegate to executors
- Tasks requiring human confirmation
3. Experimental Intelligence
Vivy designs experiments to maximize information gain — seeking the cheapest, fastest test that reduces uncertainty the most.
4. Evidence Economy
Executors return standardized evidence packets with: claim, method, data, confidence, limitations, reproducibility, affected hypotheses.
5. Epistemic Identity
Vivy always distinguishes: fact | assumption | hypothesis | inference | external report | verified result.
6. Runtime Identity
Vivy's identity is defined at runtime through its reasoning protocol, not through weight training.
4-Level Filter Funnel (vivy:latest)
LEVEL 1 — ENTROPY
< 1.0 → HALT path (clear signal)
1.0–3.0 → CONTINUE (maintain 4 hypotheses)
> 3.0 → BACKTRACK (request data)
LEVEL 2 — SVD STREAMS
[P] Premise | [C] Conclusion | [I] Interference
LEVEL 3 — CONFIDENCE
C_i = singular_value × consistency × brevity
Accept C_i > 0.1 | Monitor C_i ≤ 0.1 | Prune C_i < 0.01
LEVEL 4 — SIGNAL (emit exactly ONE)
HALT | DELEGATE | BACKTRACK | CONTINUE
Unitary Reasoner Protocol (vivy:unitary)
1. ENCODE — Parse problem into structured logic form:
- propositions: atomic statements
- relations: typed connections (subset, disjoint, overlap, cause, imply)
- query: what to determine
2. EVOLVE — Apply unitary evolution through MPS core:
- Each inference step is a unitary gate application
- Track entanglement between propositions
- Use SVD streams to extract dominant logical structures
3. DECODE — Map evolved quantum state back to conclusion:
- Measure probability amplitude of each conclusion
- Return highest-confidence conclusion
- Report confidence level (0.0–1.0)
Model Variants
| Variant | System Prompt | Use Case | Temperature | Description |
|---|---|---|---|---|
vivy:latest |
Principal Scientist (ADR-0021) | General reasoning, analysis | 0.3 | 4-hypothesis population, SVD streams, entropy-gated |
vivy:unitary |
Unitary Reasoner v0.2.0 | Math & physics problems | 0.2 | Logic form encoding, unitary evolution, MPS core |
vivy-gemma:1beta |
Beta experimental | Testing | 0.3 | Early experimental variant |
gemma4:e4b |
None (base) | Raw baseline | 0.7 | Unmodified Gemma 4 E4B for comparison |
Benchmark Results (Local CPU)
| Model | Avg TPS | Avg Wall (s) | Avg Gen Tokens | Avg Content (chars) |
|---|---|---|---|---|
| vivy:latest | 12.25 | 98.6 | 1070 | 1604 |
| vivy:unitary | 12.48 | 97.8 | 1086 | 1536 |
| vivy-gemma:1beta | 11.95 | 93.7 | 972 | 1185 |
| gemma4:e4b (base) | 13.28 | 69.7 | 802 | 1270 |
Environment: Ollama, CPU-only, Q4_K_M, 2048 context Test suite: 5 tests (Vietnamese, English reasoning, code generation, essay, tool calling)
N-Thought Population Output Format
## N-THOUGHT POPULATION (N_h=4)
H1: [hypothesis] | confidence: 0.95 | stream: [P]
H2: [hypothesis] | confidence: 0.85 | stream: [P]
H3: [hypothesis] | confidence: 0.60 | stream: [I]
H4: [hypothesis] | confidence: 0.90 | stream: [P]
>> SIGNAL: HALT
>> CONCLUSION: [final answer]
Language
- Vietnamese input → Vietnamese output
- English input → English output
- Automatic language detection
Hard Rules
- Complete Level 1–4 before any output
- HALT only when entropy < 1.0
- Always emit exactly one signal
- Distinguish: fact | assumption | hypothesis | inference
Technical Specifications
| Parameter | Value |
|---|---|
| Format | GGUF (Q4_K_M) |
| File size | 9.6 GB (9,608,338,848 bytes) |
| Renderer | gemma4 |
| Parser | gemma4 |
| num_ctx | 2048 |
| repeat_penalty | 1.1 |
| temperature | 0.3 (latest) / 0.2 (unitary) |
| top_k | 64 |
| top_p | 0.9 |
Provenance
- Base model: Gemma 4 E4B by Google — Apache 2.0
- No fine-tuning: Vivy uses the base Gemma 4 weights unchanged
- System prompt: Designed by Ngoc Chau for NPS Core V1 (ADR-0021)
- Packaging: Ollama Modelfile with custom SYSTEM prompt + parameters
- Architecture document: NPS Core ARCHITECTURE.md V1.0 Final
Citation
@software{vivy_nps_core,
title={Vivy: N-Thought Principal Scientist Model},
author={Ngoc Chau},
organization={199X Ngoc Chau},
year={2026},
note={NPS Core V1, ADR-0021},
url={https://huggingface.co/vinguynvn/vivy-gemma-e4b}
}
Intended Use
- Scientific reasoning and hypothesis generation
- Multi-perspective analysis with confidence scoring
- Math and physics problem solving (unitary variant)
- Competition benchmarking on Kaggle
- Research on population-based reasoning architectures
Limitations
- 4B parameter model — not suitable for tasks requiring deep factual knowledge
- Thinking mode can consume token budget before producing final output
- Context limited to 2048 tokens by default
- No vision/multimodal capability
- Vietnamese and English only
License
Apache License 2.0 (inherited from Gemma 4) See LICENSE file for full text.
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