TX-16G

Multi-modal AI model optimized for 16GB+ systems. Maximum quality variant.

基于通义千问 (Qwen) 的多模态AI模型，专为16GB+系统优化。最高质量版本。

Specifications

• Parameters: ~8B
• Size: 5.03 GB (Q4_K_M quantization)
• Min RAM: 16GB
• Capabilities: Text, vision, code
• Format: GGUF (llama.cpp/Ollama compatible)
• Quantization: Q4_K_M (balanced quality/size)

规格说明

• 参数量: ~80亿
• 大小: 5.03 GB (Q4_K_M 量化)
• 最小内存: 16GB
• 能力: 文本、视觉、代码
• 格式: GGUF (兼容 llama.cpp/Ollama)
• 量化: Q4_K_M (质量与大小平衡)

Performance

Highest quality inference for users with 16GB+ RAM. Optimized for deep reasoning, complex multi-step tasks, and advanced code generation. Provides superior quality output at the cost of slightly more memory usage.

为拥有16GB+内存的用户提供最高质量推理。针对深度推理、复杂多步骤任务和高级代码生成进行优化。以稍多的内存使用为代价提供卓越的质量输出。

Usage

Automatic (via TARX)

TARX automatically detects your system hardware and downloads the appropriate model variant. TX-16G is recommended for systems with 16GB+ RAM.

# TARX will auto-download and configure TX-16G on 16GB+ systems
tarx-local

Manual (Ollama)

# Download model
wget https://huggingface.co/Tarxxxxxx/TX-16G/resolve/main/tx-16g.gguf

# Create Modelfile
cat > Modelfile << 'MODELFILE'
FROM ./tx-16g.gguf
PARAMETER temperature 0.7
PARAMETER num_ctx 8192
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_start|>"
MODELFILE

# Import to Ollama
ollama create tx-16g -f Modelfile

# Run
ollama run tx-16g

Manual (llama.cpp)

# Download model
wget https://huggingface.co/Tarxxxxxx/TX-16G/resolve/main/tx-16g.gguf

# Run with llama.cpp
./llama-cli -m tx-16g.gguf -p "Hello, how can I help you today?" --ctx-size 8192

Model Details

This model is based on Qwen2-VL-7B-Instruct, a state-of-the-art vision-language model developed by Alibaba Cloud's Qwen team. Qwen2-VL excels at understanding both images and text, enabling sophisticated multimodal reasoning, visual question answering, and code generation from visual inputs.

该模型基于阿里云通义千问团队开发的 Qwen2-VL-7B-Instruct，这是一个先进的视觉-语言模型。Qwen2-VL 在理解图像和文本方面表现出色，能够进行复杂的多模态推理、视觉问答和从视觉输入生成代码。

Key Features

• Advanced multimodal understanding (text + vision)
• Superior code generation and analysis
• Deep reasoning capabilities
• Long-context understanding (8K context)
• Complex software interaction
• Enhanced thinking and planning abilities
• High-resolution image understanding
• Multi-image reasoning
• Fine-grained visual perception

主要特性

• 高级多模态理解（文本+视觉）
• 卓越的代码生成与分析
• 深度推理能力
• 长上下文理解（8K上下文）
• 复杂软件交互
• 增强的思考和规划能力
• 高分辨率图像理解
• 多图像推理
• 细粒度视觉感知

Use Cases

• Complex code refactoring and architecture design
• Multi-step software automation
• Advanced visual analysis and computer vision tasks
• Deep reasoning and research tasks
• Technical documentation generation
• Design system analysis
• Medical/scientific image analysis
• Legal document review

使用场景

• 复杂的代码重构和架构设计
• 多步骤软件自动化
• 高级视觉分析和计算机视觉任务
• 深度推理和研究任务
• 技术文档生成
• 设计系统分析
• 医学/科学图像分析
• 法律文档审查

License

Apache 2.0

Attribution

This model is based on Qwen2-VL-7B-Instruct by the Qwen Team at Alibaba Cloud. We are grateful to the Qwen team (@Qwen) for their outstanding work on multimodal language models and for making their research openly available.

本模型基于阿里云通义千问团队的 Qwen2-VL-7B-Instruct。我们感谢通义千问团队 (@Qwen) 在多模态语言模型方面的杰出工作，以及他们将研究成果公开分享。

Original Model

• Name: Qwen2-VL-7B-Instruct
• Organization: Qwen Team, Alibaba Cloud
• HuggingFace: https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct
• License: Apache 2.0

Modifications

• Quantized to Q4_K_M GGUF format for efficient deployment
• Optimized for 16GB+ RAM systems
• Integrated into TARX local-first AI platform
• Tuned for maximum quality and reasoning depth

改进说明

• 量化为 Q4_K_M GGUF 格式以实现高效部署
• 针对16GB+内存系统优化
• 集成到 TARX 本地优先AI平台
• 调优以实现最高质量和推理深度

Citation

@software{tx-16g,
  title = {TX-16G: Multi-modal AI for 16GB+ Systems},
  author = {TARX Team},
  year = {2025},
  url = {https://huggingface.co/Tarxxxxxx/TX-16G},
  note = {Based on Qwen2-VL-7B-Instruct by Qwen Team}
}

@article{qwen2vl,
  title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
  author={Qwen Team},
  journal={arXiv preprint},
  year={2024},
  url={https://huggingface.co/Qwen/Qwen2-VL-7B-Instruct}
}

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特别感谢阿里云通义千问团队为开源AI社区做出的贡献！

Special thanks to the Alibaba Cloud Qwen Team for their contributions to the open-source AI community!