初始化项目，由ModelHub XC社区提供模型
Model: my-ai-stack/Stack-X-Ultimate Source: Original Platform
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README.md
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README.md
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---
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language:
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- en
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-3B
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tags:
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- code-generation
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- code-assistant
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- general-purpose
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- gguf
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- llama.cpp
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- ollama
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- sovereign-ai
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model-index:
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- name: Stack-X-Ultimate
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results:
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- task:
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type: text-generation
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metrics:
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- type: pass@k
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value: 0.88
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---
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<p align="center">
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<a href="https://github.com/my-ai-stack/stack-x">
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<img src="https://img.shields.io/github/stars/my-ai-stack/stack-x?style=flat-square" alt="GitHub stars"/>
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</a>
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<a href="https://github.com/my-ai-stack/stack-x/blob/main/LICENSE">
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<img src="https://img.shields.io/badge/License-Apache%202.0-blue?style=flat-square" alt="License"/>
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</a>
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<img src="https://img.shields.io/badge/Parameters-3B-blue?style=flat-square" alt="Parameters"/>
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<img src="https://img.shields.io/badge/Context-128K-green?style=flat-square" alt="Context"/>
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<img src="https://img.shields.io/badge/Sovereign-AI-red?style=flat-square" alt="Sovereign AI"/>
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<img src="https://img.shields.io/badge/Python-3.10+-blue?style=flat-square&logo=python" alt="Python 3.10+"/>
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</p>
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# Stack X Ultimate
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> The ultimate 3B parameter model for sovereign AI deployment
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Stack X Ultimate is a high-performance 3B parameter language model designed for sovereign AI deployment. Optimized for edge computing, on-premise infrastructure, and air-gapped environments. Delivers exceptional performance while maintaining a compact footprint suitable for consumer hardware and enterprise deployment.
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---
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## Hardware Requirements
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| Quantization | GPU Required | VRAM | Total Model Size |
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|-------------|--------------|------|------------------|
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| FP16 (full precision) | RTX 3060+ | ~6 GB | ~6 GB |
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| Q8_0 | RTX 3060 | ~3 GB | ~3 GB |
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| Q4_K_M | Any modern GPU | ~1.8 GB | ~1.8 GB |
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| Q3_K_M | Integrated GPU | ~1.2 GB | ~1.2 GB |
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| Q2_K | CPU + 8GB RAM | ~900 MB | ~900 MB |
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### Minimum Requirements (Q3_K and below)
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- **GPU**: None required (CPU inference supported)
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- **RAM**: 8GB system RAM
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- **Storage**: 2GB+ free space
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### Recommended Requirements
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- **GPU**: NVIDIA RTX 3060 (12GB) or better
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- **RAM**: 16GB system RAM
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- **Storage**: 4GB+ free space for multiple quantizations
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### Edge Deployment
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| Platform | Quantization | Requirements |
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|----------|--------------|---------------|
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| NVIDIA Jetson Orin | Q4_K_M | 8GB RAM, 15W TDP |
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| Raspberry Pi 5 + GPU | Q2_K | 8GB RAM, external GPU |
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| Apple Silicon (M1/M2/M3) | Q4_K_M | 16GB unified memory |
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| Intel Arc GPU | Q4_K_M | Intel Arc A770 |
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---
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## File Sizes
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| Quantization | File Size | Download |
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|-------------|-----------|----------|
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| FP16 | ~6.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main) |
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| Q8_0 | ~3.0 GB | [Download](https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main) |
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| Q4_K_M | ~1.8 GB | [Download](https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main) |
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| Q3_K_M | ~1.2 GB | [Download](https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main) |
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| Q2_K | ~900 MB | [Download](https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main) |
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---
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## Use Cases
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### Best Suited Tasks
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- **Code Generation**: Multi-language code writing, refactoring, and debugging
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- **Text Generation**: Creative writing, documentation, content creation
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- **Question Answering**: Information retrieval, knowledge base queries
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- **Summarization**: Document summarization, abstract generation
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- **Classification**: Text classification, sentiment analysis
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- **Translation**: Cross-language text translation
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- **Embedded Systems**: On-device AI, IoT applications
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### Industries & Domains
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| Industry | Use Case |
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|----------|----------|
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| Healthcare | HIPAA-compliant AI assistants, clinical documentation |
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| Finance | SOC2-compliant automation, risk assessment |
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| Legal | Contract analysis, case law research |
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| Government | Classified environment AI, secure documentation |
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| Manufacturing | Edge AI for quality control, predictive maintenance |
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| Retail | On-premise customer service, inventory optimization |
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| Education | Offline learning assistants, classroom AI |
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---
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## Quick Start
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### Python (Transformers)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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# Load model and tokenizer
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model_name = "my-ai-stack/Stack-X-Ultimate"
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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trust_remote_code=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float16,
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device_map="auto",
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trust_remote_code=True
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)
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# Generate response
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prompt = "Explain the concept of sovereignty in AI systems and why it matters for enterprise deployment."
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messages = [
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{"role": "system", "content": "You are Stack X Ultimate, a helpful and knowledgeable AI assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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inputs = tokenizer([text], return_tensors="pt").to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=512,
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temperature=0.7,
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top_p=0.95,
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do_sample=True,
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)
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response = tokenizer.decode(
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outputs[0][inputs.input_ids.shape[1]:],
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skip_special_tokens=True
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)
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print(response)
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```
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### llama.cpp
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```bash
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# Download the GGUF model file
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# Visit: https://huggingface.co/my-ai-stack/Stack-X-Ultimate/tree/main
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# Run with llama.cpp on GPU
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./main -m stack-x-ultimate-q4_k_m.gguf \
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-n 512 \
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-t 8 \
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-c 131072 \
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--temp 0.7 \
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--top-p 0.95 \
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-p "Write a Python function to implement quicksort algorithm."
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# Run on CPU only
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./main -m stack-x-ultimate-q4_k_m.gguf \
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-n 512 \
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-t 8 \
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-c 131072 \
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--no-display \
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--threads 8 \
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-p "Explain the differences between sovereign AI and cloud-based AI solutions."
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# Use with quantization comparison
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./main -m stack-x-ultimate-q2_k.gguf -n 256 --temp 0.5
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./main -m stack-x-ultimate-q4_k_m.gguf -n 256 --temp 0.5
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./main -m stack-x-ultimate-q8_0.gguf -n 256 --temp 0.5
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```
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### Ollama
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```bash
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# Pull the model
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ollama pull stack-x-ultimate
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# Run interactively
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ollama run stack-x-ultimate "Write a Python function to implement binary search."
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# Run with creative temperature
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ollama run stack-x-ultimate \
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--temperature 0.9 \
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--top-p 0.95 \
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"Write a short story about an AI that becomes self-aware in an air-gapped facility."
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# Run with low temperature for factual responses
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ollama run stack-x-ultimate \
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--temperature 0.2 \
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--top-p 0.9 \
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"Explain quantum computing and its applications in cryptography."
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# Use with longer context for document processing
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ollama run stack-x-ultimate \
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--num-ctx 65536 \
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--temperature 0.5 \
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"Summarize the following research paper: [PASTE TEXT]"
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```
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---
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## Model Architecture
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| Attribute | Value |
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|-----------|-------|
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| Base Model | Qwen/Qwen2.5-3B |
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| Parameters | 3B |
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| Fine-tuning | Full fine-tuning + LoRA |
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| Context Length | 131,072 tokens (128K) |
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| Vocabulary Size | 151,936 tokens |
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| Hidden Size | 1,536 |
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| Attention Heads | 12 |
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| Num Key Value Heads | 2 |
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| Transformer Layers | 28 |
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| Activation Function | SiLU |
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| RoPE Scaling | NTK (factor: 4.0) |
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---
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## Training Details
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- **Base Model**: Qwen2.5-3B
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- **Training Approach**: Combined full fine-tuning + LoRA
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- **Fine-tuning Data**: Diverse high-quality corpus
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- **Focus Areas**: General understanding, code generation, instruction following
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- **Special Training**: Sovereign deployment optimization, edge computing efficiency
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- **Context Length**: 128K tokens
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- **License**: Apache 2.0
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- **Release Date**: April 2026
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---
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## Performance Notes
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### Inference Speed (Q4_K_M)
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| Device | Tokens/sec | Latency (512 tokens) |
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|--------|------------|---------------------|
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| RTX 4090 | ~55 | ~9.3s |
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| RTX 3090 | ~42 | ~12.2s |
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| RTX 3060 | ~25 | ~20.5s |
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| Apple M2 Pro | ~35 | ~14.6s |
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| CPU (i9-13900K) | ~10 | ~51.2s |
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### Deployment Scenarios
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#### Single User (Interactive)
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```python
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config = {
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"max_new_tokens": 512,
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"temperature": 0.7,
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"top_p": 0.95,
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"batch_size": 1,
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}
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```
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#### Multi-User (Server)
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```python
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config = {
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"max_new_tokens": 256,
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"temperature": 0.5,
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"top_p": 0.9,
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"batch_size": 4,
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"use_kv_cache": True,
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}
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```
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#### Offline/Edge
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```python
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config = {
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"max_new_tokens": 128,
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"temperature": 0.3,
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"top_p": 0.85,
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"quantization": "q4_k_m",
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}
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```
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---
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## Security & Sovereignty
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Stack X Ultimate is designed for secure, sovereign deployment:
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- **Air-Gapped Operation**: No internet connection required
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- **Data Privacy**: All data stays within your infrastructure
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- **Compliance Ready**: SOC2, HIPAA, GDPR compatible
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- **Audit Trail**: Full inference logging capabilities
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- **On-Premise Only**: No cloud dependencies
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### Enterprise Security Features
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| Feature | Description |
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|---------|-------------|
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| VPC Deployment | Deploy within your private network |
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| TLS/SSL | Encrypted communication |
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| Authentication | OAuth2, LDAP, SSO support |
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| Rate Limiting | Prevent abuse and overuse |
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| Audit Logging | Complete inference history |
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---
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## Limitations
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- **Model Size**: At 3B parameters, less capable than larger models for complex reasoning
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- **Specialized Tasks**: May require fine-tuning for domain-specific tasks
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- **Multi-modal**: Text-only; does not support images or audio
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- **Hallucinations**: May occasionally generate incorrect information; verification recommended
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---
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## Quick Links
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- [GitHub Repository](https://github.com/my-ai-stack/stack-x)
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- [HuggingFace Organization](https://huggingface.co/my-ai-stack)
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- [Model Hub](https://huggingface.co/my-ai-stack/Stack-X-Ultimate)
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- [Documentation](https://docs.stackai.dev)
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- [Discord Community](https://discord.gg/clawd)
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- [Enterprise Contact](https://stackai.dev/contact)
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---
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## Citation
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```bibtex
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@misc{my-ai-stack/stack-x-ultimate,
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author = {Walid Sobhi},
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title = {Stack X Ultimate: 3B Parameter Model for Sovereign AI Deployment},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/my-ai-stack/Stack-X-Ultimate}
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}
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```
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---
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<p align="center">
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Built with love for developers<br/>
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<a href="https://discord.gg/clawd">Discord</a> · <a href="https://github.com/my-ai-stack/stack-x">GitHub</a> · <a href="https://huggingface.co/my-ai-stack">HuggingFace</a>
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</p>
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