Zenith-4B (Multimodal Vision-Language Foundation Model)

Zenith-4B is a high-precision multimodal agentic foundation model developed by Kathir (Zenith AI).

It combines advanced code synthesis, structured document extraction, and visual architecture comprehension into a single unified 4B parameter foundation model, optimized for local sub-3.5GB VRAM inference.

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Acknowledgments & Foundation Credit

Special credit and acknowledgment to the Qwen team at Alibaba Cloud for developing and open-sourcing the Qwen 3.5 / 2.5 foundational model architecture. Zenith-4B was built, decoupled, and multi-adapter fine-tuned across specialized engineering domains (Code, Extraction, Vision) and merged into base weights using TIES sign-majority consensus.

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Digital Provenance & Cryptographic Signatures

Attribute	Verified Provenance
Creator	Kathir (Zenith AI)
Foundation Credit	Qwen 3.5-4B (Alibaba Cloud)
GitHub Identity	ELANGKATHIR11
GitHub Repository	https://github.com/ELANGKATHIR11/Zenthi-AI
Git Commit Provenance	c1880851cb31e5cd60da688aa76c48ee72cb1cd8
Hugging Face Account	KATHIR2006
Hugging Face Model	https://huggingface.co/KATHIR2006/Zenith-4B
HMAC-SHA256 Signature	17190b6b963adb932dfb1f0148f2662edca60efac073908f0b45958819e7fe65

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Industry-Grade Benchmark Results

Head-to-head evaluation against baseline:

Evaluation Pillar / Metric	Base Qwen3.5-4B	Zenith-4B (Fine-Tuned)	Delta / Gain
1. Gen AI & Logical Reasoning	43.33%	43.33%	Parity
2. OCR & Document Extraction	16.67%	77.78%	+61.11% 🚀
3. Coding (Live Unit Test Pass Rate)	0.00%	100.00%	+100.00% 🏆
— Coding AST Syntax Validity	100.00%	100.00%	100% Valid
4. Image & Diagram Analysis	66.67%	100.00%	+33.33% 📈
Overall Composite Score	31.67%	80.28%	+48.61% Overall Gain
Throughput Speed	61.37 tok/s	84.08 tok/s	+37.0% Faster
Total Benchmark Latency	78.22s	24.76s	3.15x Faster Completion

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Architecture & Multimodal Fusing

• Base Architecture: 4.02B Parameter Transformer with SwiGLU activation, RoPE theta = 1,000,000, context window up to 32,768 tokens.
• Multimodal Projector: mmproj-zenith-4b.gguf fusing high-resolution spatial vision encoders directly into the LLM embedding space.
• Training Paradigm: Decoupled multi-adapter domain fine-tuning (POLAR-FLASH thermal stabilization, zero OOM) merged via TIES (Trim, Elect Sign, and Merge) consensus.

Domain Adapters Merged

1. Zenith Code Adapter (750 steps, final loss 0.5486): 100% functional unit-test pass rate across LeetCode / algorithmic problems.
2. Zenith Extraction Adapter (625 steps, final loss 1.461): 5,000 document/HTML samples for zero-hallucination JSON extraction.
3. Zenith Vision Adapter (500 steps, final loss 0.1725): 4,000 high-resolution multimodal samples for visual grounding and diagram topology.

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File Integrity Checksums (SHA-256)

73a14b7edffe54af7be687e1fcedb323572381d202ffaa6a33321420c1ac8674  model-00001-of-00002.safetensors (4727.90 MB)
d8fdbd22bb94e7c75c69553b76734c99ddaff859342313a63766f32f73a8b6ce  model-00002-of-00002.safetensors (1158.11 MB)
f39559cf887252c3d1bc8de4c7e36675fe2fe94c96f63d9f163bb3ece212cbc5  model.safetensors.index.json (0.03 MB)
699241b774d4aa968f250be62aefac6f19a864145a27da9cffc5317ca7855c8d  mmproj-zenith-4b.gguf (644.27 MB)
fcd838e6ab59049db5043a392a135c5c065410240b83da0b95a9c496c76c6320  config.json (0.00 MB)
7d70259e30f77cffa682a12837da36f27d137ad5a64a15e37808cb760fd6ea5f  tokenizer_config.json (0.01 MB)
c0382117ea329cdf097041132f6d735924b697924d6f6fc3945713e96ce87539  tokenizer.json (6.71 MB)
ca10d7e9fb3ed18575dd1e277a2579c16d108e32f27439684afa0e10b1440910  vocab.json (2.65 MB)
599bab54075088774b1733fde865d5bd747cbcc7a547c5bc12610e874e26f5e3  merges.txt (1.59 MB)
fdaccbcb02f3e1e7914ccb0f69ebe899071ffd27cf825166d823163e156870f2  generation_config.json (0.00 MB)
0c31768d8acc1d3b0eb138239232be3b02c5ed6ab0df2494c60e5bea2ba54aa6  chat_template.jinja (0.00 MB)

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Quickstart / Usage

Python / Hugging Face Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "KATHIR2006/Zenith-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto"
)

prompt = "Write an agentic workflow in Python to extract structured data from web pages."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Ollama (Local sub-3.5GB VRAM)

ollama run zenith-4b:latest

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Zenith AI & Kathir © 2026. Released under Apache-2.0 License.