VTXAI/vtx-embed-7M-lf2
Feature Extraction • Updated
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Abhaykoul
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Shipped v0.1.2 of vtx — a minimalist coding agent for the terminal.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Most agentic CLIs ship 10k+ token system prompts. Vtx is ~2,200. Less prompt overhead means more room for your code in the model's context window.
Vtx is a from-scratch Python implementation of the design philosophy behind pi-mono — same principles, pure Python, no transpiled runtime.
What ships out of the box:
→ Textual TUI + headless CLI (vtx -p "fix the failing test")
→ 49 LLM provider gateways, all declared in a single provider.yaml
→ 5 core tools (read / edit / write / bash / find) plus web search and fetch
→ Session tree with compaction, handoff, and resume
→ AGENTS.md / CLAUDE.md auto-discovery
→ Skills system — drop SKILL.md files in .agents/skills/ and they become slash commands
→ Two OAuth flows (GitHub Copilot device flow, OpenAI Codex PKCE)
→ Two-mode permissions: prompt (default) or auto, with a safe-command allowlist
This release adds a proper extension system. Register new LLM-callable tools, intercept tool calls, hook lifecycle events, and add slash commands from a single register(api) function in a Python file under ~/.vtx/agent/extensions/. Extensions can override built-in tools by name and chain handler logic across subscribers.
Apache 2.0. uv tool install vtx-coding-agent and you're running.
GitHub: https://github.com/OEvortex/vtx-coding-agent
PyPI: https://pypi.org/project/vtx-coding-agent
Built in the open. Feedback, extensions, and PRs welcome.
Abhaykoul
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model 4 months ago
Abhaykoul
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model 4 months ago
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3442
🚀 Ever dreamed of training your own Large Language Model from scratch? What if I told you it doesn't require a supercomputer or PhD in ML? 🤯
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. 💻➡️🖥️
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
🎓 Educational transparency - every component built from scratch with clear code
💻 CPU-first approach - start training immediately, no GPU needed
🔧 Full customization - modify anything you want
📈 Seamless scaling - from laptop to cluster without code changes
🤝 HuggingFace integration - works with existing models & tokenizers
Key highlights:
✅ Built-in tokenizers (BPE, WordPiece, HF wrappers)
✅ Complete Transformer implementation from scratch
✅ Optimized for CPU training
✅ Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
✅ Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
🔗 Check it out: https://github.com/HelpingAI/llm-trainer
📚 Docs: Getting Started Guide
💬 Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! 🙏
Introducing LLM Trainer - the educational framework that makes LLM training accessible to EVERYONE! Whether you're on a CPU-only laptop or scaling to distributed GPUs, we've got you covered. 💻➡️🖥️
Why LLM Trainer? Because existing tools are either too simplistic (hiding the magic) or too complex (requiring expert knowledge). We bridge the gap with:
🎓 Educational transparency - every component built from scratch with clear code
💻 CPU-first approach - start training immediately, no GPU needed
🔧 Full customization - modify anything you want
📈 Seamless scaling - from laptop to cluster without code changes
🤝 HuggingFace integration - works with existing models & tokenizers
Key highlights:
✅ Built-in tokenizers (BPE, WordPiece, HF wrappers)
✅ Complete Transformer implementation from scratch
✅ Optimized for CPU training
✅ Advanced features: mixed precision, gradient checkpointing, multiple generation strategies
✅ Comprehensive monitoring & metrics
Perfect for:
- Students learning transformers
- Researchers prototyping new ideas
- Developers building domain-specific models
Ready to train your first LLM? It's easier than you think!
🔗 Check it out: https://github.com/HelpingAI/llm-trainer
📚 Docs: Getting Started Guide
💬 Join the community: GitHub Discussions
#AI #MachineLearning #LLM #DeepLearning #OpenSource #Python #HuggingFace #NLP
Special thanks to HuggingFace and PyTorch teams for the amazing ecosystem! 🙏
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