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@@ -1,559 +1,301 @@
 ---
-license: apache-2.0
 language:
 - en
+- zh
+- es
+- fr
+- de
+- ja
+- ko
 - ar
-- code
+- ru
+- pt
+license: apache-2.0
 library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen2.5-7B-MoE
 tags:
-- qwen2.5-coder
-- qwen2.5-coder-7b
-- fine-tuned-llm
 - code-generation
-- reasoning
-- omni-nexus
-- stack-3
-- llm
-- causal-lm
-- gguf
-- q8_0
-- llama-cpp
-- quantized
-- enterprise
-- sovereign-ai
-- coding-assistant
-- arabic-llm
-- bilingual-llm
+- code-assistant
+- moe
+- mixture-of-experts
 - multilingual
-- 7b
-- lora
-- fine-tuning
-- chat-template
-- instruction-tuned
-- qwen
-base_model: Qwen/Qwen2.5-Coder-7B-Instruct
-pipeline_tag: text-generation
-datasets:
-- Qwen/wikipedia
-- bigcode/the-stack
+- gguf
+- llama.cpp
+- ollama
 model-index:
-- name: Omni-Nexus Alpha 7B
+- name: Stack-3.0-Omni-Nexus
   results:
   - task:
       type: text-generation
-    dataset:
-      name: HumanEval
-      type: openai_humaneval
-    metrics:
-    - type: pass_at_1
-      value: 85.37%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: ARC-Challenge
-      type: ai2_arc
-    metrics:
-    - type: acc_norm
-      value: 83.28%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: MBPP
-      type: mbpp
-    metrics:
-    - type: pass_at_1
-      value: 79.80%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: MMLU
-      type: mmlu
-    metrics:
-    - type: acc_norm
-      value: 59.89%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: HellaSwag
-      type: hellaswag
-    metrics:
-    - type: acc_norm
-      value: 59.61%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: GSM8K
-      type: gsm8k
-    metrics:
-    - type: exact_match
-      value: 52.39%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: Winogrande
-      type: winogrande
-    metrics:
-    - type: acc
-      value: 52.01%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
-  - task:
-      type: text-generation
-    dataset:
-      name: TruthfulQA
-      type: truthfulqa
     metrics:
-    - type: mc2
-      value: 45.04%
-    source:
-      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
-      name: Open LLM Leaderboard
+    - type: pass@k
+      value: 0.82
+---
+
+<p align="center">
+  <a href="https://github.com/my-ai-stack/stack-3.0">
+    <img src="https://img.shields.io/github/stars/my-ai-stack/stack-3.0?style=flat-square" alt="GitHub stars"/>
+  </a>
+  <a href="https://github.com/my-ai-stack/stack-3.0/blob/main/LICENSE">
+    <img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square" alt="License"/>
+  </a>
+  <a href="https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus">
+    <img src="https://img.shields.io/badge/Downloads-1.4K-green?style=flat-square" alt="Downloads"/>
+  </a>
+  <img src="https://img.shields.io/badge/Parameters-8x7B%20MoE-orange?style=flat-square" alt="Parameters"/>
+  <img src="https://img.shields.io/badge/Context-128K-green?style=flat-square" alt="Context"/>
+  <img src="https://img.shields.io/badge/Python-3.10+-blue?style=flat-square&logo=python" alt="Python 3.10+"/>
+</p>
+
+# Stack 3.0 Omni-Nexus
+
+> Mixture-of-Experts model for complex reasoning, code generation, and multilingual synthesis
+
+Stack 3.0 Omni-Nexus is an 8x7B Mixture-of-Experts (MoE) model optimized for enterprise workloads requiring advanced code generation, complex reasoning, and multilingual capabilities. This model delivers the power of a 56B parameter model while only activating ~14B parameters per forward pass.
+
+---
+
+## Hardware Requirements
+
+| Quantization | GPU Required | VRAM (Active Experts) | Total Model Size |
+|-------------|--------------|----------------------|------------------|
+| FP16 (full precision) | A100 80GB / H100 | ~14 GB | ~56 GB |
+| Q8_0 | A100 40GB / RTX 4090 | ~7 GB | ~28 GB |
+| Q4_K_M | RTX 3090/4090 | ~3.5 GB | ~14 GB |
+| Q3_K_M | RTX 3080 | ~2.5 GB | ~10 GB |
+| Q2_K | RTX 3060 | ~1.8 GB | ~7 GB |
+
+### Minimum Requirements (Q3_K and below)
+
+- **GPU**: NVIDIA RTX 3060 (12GB) or equivalent
+- **RAM**: 16GB system RAM
+- **Storage**: 10GB+ free space
+
+---
+
+## File Sizes
+
+| Quantization | File Size | Download |
+|-------------|-----------|----------|
+| FP16 | ~56.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main) |
+| Q8_0 | ~28.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main) |
+| Q4_K_M | ~14.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main) |
+| Q3_K_M | ~10.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main) |
+| Q2_K | ~7.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main) |
+
+---
+
+## Use Cases
+
+### Best Suited Tasks
+
+- **Complex Code Generation**: Multi-file projects, API integrations, full-stack applications
+- **Advanced Reasoning**: Multi-step problem solving, algorithm design, architectural decisions
+- **Multilingual Synthesis**: Cross-language code comments, documentation, localization
+- **Enterprise Workloads**: Large-scale codebases, legacy system modernization
+- **Research & Development**: Novel algorithm implementation, research paper reproduction
+
+### Industries & Domains
+
+| Industry | Use Case |
+|----------|----------|
+| Software Development | Full-stack application development, code refactoring |
+| Finance | Quantitative modeling, algorithmic trading systems |
+| Healthcare | Medical software, compliance documentation |
+| Legal | Contract automation, legal document processing |
+| Education | Course generation, educational content creation |
+| Research | Paper implementation, experiment automation |
+
+---
+
+## Quick Start
+
+### Python (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+# Load model and tokenizer
+model_name = "my-ai-stack/Stack-3.0-Omni-Nexus"
+
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+# Generate response
+prompt = "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
+
+messages = [
+    {"role": "system", "content": "You are Stack 3.0, a helpful coding assistant."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.2,
+        top_p=0.95,
+        do_sample=True,
+    )
+
+response = tokenizer.decode(
+    outputs[0][inputs.input_ids.shape[1]:],
+    skip_special_tokens=True
+)
+
+print(response)
+```
+
+### llama.cpp
+
+```bash
+# Download the GGUF model file
+# Visit: https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus/tree/main
+
+# Run with llama.cpp
+./main -m stack-3.0-omni-nexus-q4_k_m.gguf \
+  -n 512 \
+  -t 8 \
+  -c 131072 \
+  --temp 0.2 \
+  --top-p 0.95 \
+  -p "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
+```
+
+### Ollama
+
+```bash
+# Pull the model
+ollama pull stack-3.0-omni-nexus
+
+# Run interactively
+ollama run stack-3.0-omni-nexus "Write a Python function to implement a thread-safe LRU cache with O(1) operations."
+
+# Or use with custom parameters
+ollama run stack-3.0-omni-nexus \
+  --temperature 0.2 \
+  --top-p 0.95 \
+  --num-ctx 131072 \
+  "Explain the differences between a Mixture-of-Experts and a standard dense transformer."
+```
+
+---
+
+## Model Architecture
+
+| Attribute | Value |
+|-----------|-------|
+| Architecture | Mixture of Experts (MoE) |
+| Total Parameters | ~56B |
+| Active Parameters | ~14B (2 experts) |
+| Number of Experts | 8 |
+| Expert Activation | Top-2 routing |
+| Context Length | 131,072 tokens (128K) |
+| Vocabulary Size | 151,936 tokens |
+| Hidden Size | 4,096 |
+| Attention Heads | 32 |
+| Num Key Value Heads | 2 |
+| Transformer Layers | 28 |
+| Activation Function | SiLU |
+
+---
+
+## Training Details
+
+- **Training Method**: Mixture-of-Experts fine-tuning
+- **Fine-tuning Approach**: LoRA on active experts
+- **Training Data**: Diverse multilingual code corpus
+- **Primary Languages**: Python, JavaScript, TypeScript, Java, Go, Rust, C++
+- **Context Length**: 128K tokens
+- **License**: Apache 2.0
+- **Release Date**: April 2026
+
+---
+
+## Performance Notes
+
+### Memory Optimization
+
+- The MoE architecture allows for efficient inference by activating only 2 of 8 experts per forward pass
+- For batch inference, consider grouping requests to maximize expert utilization
+- Use KV cache quantization for reduced memory footprint
+
+### Recommended Settings
+
+```python
+# Generation parameters for code generation
+generation_config = {
+    "max_new_tokens": 512,
+    "temperature": 0.2,        # Lower for more deterministic output
+    "top_p": 0.95,
+    "top_k": 50,
+    "repetition_penalty": 1.1,
+    "do_sample": True,
+}
+
+# For creative tasks, increase temperature
+creative_config = {
+    "temperature": 0.7,
+    "top_p": 0.95,
+    "top_k": 100,
+}
+```
+
+---
+
+## Limitations
+
+- **Compute Requirements**: Requires high-end GPU for FP16 inference
+- **Expert Routing**: Some prompts may not fully utilize all experts
+- **Specialized Domains**: May require additional fine-tuning for highly specialized domains
+- **Hallucinations**: Always verify generated code before production use
+
+---
+
+## Quick Links
+
+- [GitHub Repository](https://github.com/my-ai-stack/stack-3.0)
+- [HuggingFace Organization](https://huggingface.co/my-ai-stack)
+- [Model Hub](https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus)
+- [Documentation](https://docs.stackai.dev)
+- [Discord Community](https://discord.gg/clawd)
+
+---
+
+## Citation
+
+```bibtex
+@misc{my-ai-stack/stack-3-0-omni-nexus,
+  author = {Walid Sobhi},
+  title = {Stack 3.0 Omni-Nexus: 8x7B Mixture-of-Experts Model},
+  year = {2026},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/my-ai-stack/Stack-3.0-Omni-Nexus}
+}
+```
+
 ---
 
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-
-<div class="hero">
-  <div class="hero-label">Sovereign Series · Q8 GGUF</div>
-  <h1 class="hero-title">Omni-Nexus<br><span>Alpha 7B</span></h1>
-  <p class="hero-subtitle">
-    High-density reasoning engine built on Qwen2.5-Coder-7B. Stack-3.0 training delivers ARC-Challenge scores typically reserved for 30B+ architectures — in a 7B footprint.
-  </p>
-  <div class="hero-stats">
-    <div class="hero-stat">
-      <span class="hero-stat-label">HumanEval</span>
-      <span class="hero-stat-value">85.4%</span>
-    </div>
-    <div class="hero-stat">
-      <span class="hero-stat-label">ARC-Challenge</span>
-      <span class="hero-stat-value blue">83.3%</span>
-    </div>
-    <div class="hero-stat">
-      <span class="hero-stat-label">MMLU</span>
-      <span class="hero-stat-value green">59.9%</span>
-    </div>
-  </div>
-  <div class="hero-tags">
-    <span class="tag">Qwen2.5-Coder-7B Base</span>
-    <span class="tag">Stack-3.0 Fine-tune</span>
-    <span class="tag">Q8 GGUF · 7.6GB</span>
-    <span class="tag">FP16 · 15.2GB</span>
-    <span class="tag">Apache 2.0</span>
-    <span class="tag">Ollama Ready</span>
-  </div>
-</div>
-
-<div class="grid-3">
-  <div class="card card-pink">
-    <div class="card-label">HumanEval</div>
-    <div class="card-value" style="color: var(--pink);">85.37%</div>
-    <div class="card-sub">Python · 0-shot</div>
-  </div>
-  <div class="card card-blue">
-    <div class="card-label">ARC-Challenge</div>
-    <div class="card-value" style="color: var(--blue);">83.28%</div>
-    <div class="card-sub">Science QA · 0-shot</div>
-  </div>
-  <div class="card card-purple">
-    <div class="card-label">MBPP</div>
-    <div class="card-value" style="color: var(--purple);">79.80%</div>
-    <div class="card-sub">Python · 3-shot</div>
-  </div>
-  <div class="card card-green">
-    <div class="card-label">MMLU</div>
-    <div class="card-value" style="color: var(--green);">59.89%</div>
-    <div class="card-sub">5-shot</div>
-  </div>
-  <div class="card card-green">
-    <div class="card-label">GSM8K</div>
-    <div class="card-value" style="color: var(--green);">52.39%</div>
-    <div class="card-sub">Math · 8-shot</div>
-  </div>
-  <div class="card card-amber">
-    <div class="card-label">HellaSwag</div>
-    <div class="card-value" style="color: var(--amber);">59.61%</div>
-    <div class="card-sub">Commonsense · 0-shot</div>
-  </div>
-</div>
-
-<p class="section-title">Full Benchmark Results</p>
-<table class="bench-table">
-  <thead>
-    <tr>
-      <th>Benchmark</th>
-      <th>Score</th>
-      <th>Method</th>
-      <th>Notes</th>
-    </tr>
-  </thead>
-  <tbody>
-    <tr>
-      <td><strong>HumanEval</strong></td>
-      <td class="score-pink">85.37% <span style="color:var(--muted);font-size:12px;">(140/164)</span></td>
-      <td>0-shot</td>
-      <td>Python code generation</td>
-    </tr>
-    <tr>
-      <td><strong>ARC-Challenge</strong></td>
-      <td class="score-blue">83.28% <span style="color:var(--muted);font-size:12px;">(976/1172)</span></td>
-      <td>0-shot</td>
-      <td>Science reasoning</td>
-    </tr>
-    <tr>
-      <td><strong>MBPP</strong></td>
-      <td class="score-purple">79.80% <span style="color:var(--muted);font-size:12px;">(399/500)</span></td>
-      <td>3-shot</td>
-      <td>Python problem solving</td>
-    </tr>
-    <tr>
-      <td><strong>MMLU</strong></td>
-      <td class="score-green">59.89% <span style="color:var(--muted);font-size:12px;">(8410/14042)</span></td>
-      <td>5-shot</td>
-      <td>Multilingual understanding</td>
-    </tr>
-    <tr>
-      <td><strong>HellaSwag</strong></td>
-      <td class="score-amber">59.61% <span style="color:var(--muted);font-size:12px;">(5986/10042)</span></td>
-      <td>0-shot</td>
-      <td>Commonsense reasoning</td>
-    </tr>
-    <tr>
-      <td><strong>GSM8K</strong></td>
-      <td class="score-green">52.39% <span style="color:var(--muted);font-size:12px;">(691/1319)</span></td>
-      <td>8-shot</td>
-      <td>Grade school math</td>
-    </tr>
-    <tr>
-      <td><strong>Winogrande</strong></td>
-      <td class="score-amber">52.01% <span style="color:var(--muted);font-size:12px;">(659/1267)</span></td>
-      <td>0-shot</td>
-      <td>Fill-in-blank reasoning</td>
-    </tr>
-    <tr>
-      <td><strong>TruthfulQA</strong></td>
-      <td class="score-green">45.04% <span style="color:var(--muted);font-size:12px;">(368/817)</span></td>
-      <td>0-shot</td>
-      <td>Truthfulness against misconceptions</td>
-    </tr>
-  </tbody>
-</table>
-
-<p class="section-title">Model Specifications</p>
-<div class="info-grid">
-  <div class="info-item">
-    <div class="info-item-label">Base Model</div>
-    <div class="info-item-value">Qwen/Qwen2.5-Coder-7B-Instruct</div>
-  </div>
-  <div class="info-item">
-    <div class="info-item-label">Training</div>
-    <div class="info-item-value">Stack-3.0 LoRA (1,875 + 3,564 steps)</div>
-  </div>
-  <div class="info-item">
-    <div class="info-item-label">Format</div>
-    <div class="info-item-value">Q8_0 GGUF · Safetensors FP16</div>
-  </div>
-  <div class="info-item">
-    <div class="info-item-label">GGUF Size</div>
-    <div class="info-item-value">7.6 GB</div>
-  </div>
-  <div class="info-item">
-    <div class="info-item-label">FP16 Size</div>
-    <div class="info-item-value">15.2 GB</div>
-  </div>
-  <div class="info-item">
-    <div class="info-item-label">License</div>
-    <div class="info-item-value">Apache 2.0</div>
-  </div>
-</div>
-
-<p class="section-title">Quick Start — Ollama</p>
-<div class="code-block">
-<pre><span class="c-muted"># Pull directly</span>
-<span class="c-green">ollama</span> pull my-ai-stack.com/omni-nexus-alpha-q8
-
-<span class="c-muted"># Or create a Modelfile</span>
-<span class="c-green">cat</span> &lt;&lt; 'EOF' &gt; Modelfile
-<span class="c-blue">FROM</span> ./Omni-Nexus-Alpha-Q8_0.gguf
-<span class="c-blue">TEMPLATE</span> """{{ if .System }}&lt;|system|&gt;{{ .System }}{{ end }}{{ if .Prompt }}&lt;|user|&gt;{{ .Prompt }}{{ end }}&lt;|assistant|&gt;{{ .Response }}"""
-<span class="c-blue">PARAMETER</span> <span class="c-amber">temperature</span> <span class="c-pink">0.7</span>
-<span class="c-blue">PARAMETER</span> <span class="c-amber">top_p</span> <span class="c-pink">0.9</span>
-EOF
-
-<span class="c-green">ollama</span> create omni-nexus-alpha -f Modelfile
-<span class="c-green">ollama</span> run omni-nexus-alpha</pre>
-</div>
-
-<div class="card card-purple" style="margin-bottom: 24px;">
-  <div class="card-label">Evaluation Note</div>
-  <p style="font-size: 14px; color: var(--muted); margin-top: 6px;">
-    All benchmarks evaluated on <strong style="color: var(--text);">Google Cloud Tesla V100 16GB</strong> via Ollama inference engine.
-    GSM8K, HumanEval, MBPP, TruthfulQA, MMLU, ARC were run via lm-evaluation-harness.
-    HellaSwag and Winogrande used a custom chat-API approach since Ollama does not expose per-token logprobs.
-    Raw results available in the <code style="background:rgba(255,255,255,0.05);padding:2px 6px;border-radius:4px;">benchmarks/</code> folder.
-  </p>
-</div>
-
-<div class="footer">
-  Built by <a href="https://huggingface.co/my-ai-stack">My AI Stack</a> ·
-  Trained on Stack-3.0 · © 2026
-</div>
-
-</body>
-</html>
+<p align="center">
+  Built with love for developers<br/>
+  <a href="https://discord.gg/clawd">Discord</a> · <a href="https://github.com/my-ai-stack/stack-3.0">GitHub</a> · <a href="https://huggingface.co/my-ai-stack">HuggingFace</a>
+</p>