Can I run these finetuned models locally?

Yes, this skill includes instructions for exporting your trained model to GGUF format, which can be run locally using Ollama or llama.cpp.

How much does it cost to generate a training dataset?

Costs are very low; generating 1000 examples ranges from $0.02 using DeepSeek to roughly $0.50 using Claude 3.5 Sonnet.

What models are recommended for Slipstream finetuning?

GLM-4-9B-0414 is highly recommended because it is MIT licensed, optimized for agentic tasks and function calling, and fits within 8GB of VRAM using 4-bit quantization.

Do I need a high-end GPU for training?

By using Unsloth and QLoRA, you can finetune a 9B parameter model on a consumer GPU with as little as 8GB to 16GB of VRAM, or even on free-tier Google Colab instances.

Slipstream LLM Finetuning

Name: Slipstream LLM Finetuning
Author: anthony-maio

byanthony-maio

•

데이터 과학 및 ML

Optimizes large language models to communicate natively through the Slipstream inter-agent protocol using efficient finetuning workflows.

소개

This skill provides a complete, end-to-end framework for training LLMs to speak the Slipstream protocol natively, moving beyond simple prompting for agentic coordination. It streamlines the entire machine learning lifecycle—from generating high-quality synthetic datasets via major LLM providers like Claude and DeepSeek to performing memory-efficient QLoRA finetuning with Unsloth. By following this guide, developers can create specialized, high-performance agent models optimized for strict protocol compliance and ready for deployment via HuggingFace or local tools like Ollama.

주요 기능

Multi-format model export including LoRA adapters, merged weights, and GGUF
Automated dataset generation using templates or LLM synthesis via Claude, OpenAI, and DeepSeek
Ready-to-use Modelfile generation for local deployment in Ollama
Optimized training templates for GLM-4-9B and other agent-centric models
1 GitHub stars
Memory-efficient QLoRA finetuning using Unsloth to fit 9B models in consumer VRAM

사용 사례

Training lightweight models to handle complex inter-agent communication protocols without prompt overhead
Developing high-performance, cost-effective local LLMs for private agentic workflows
Standardizing model outputs to strictly follow the SLIP wire format for system interoperability

소개

주요 기능

Multi-format model export including LoRA adapters, merged weights, and GGUF
Automated dataset generation using templates or LLM synthesis via Claude, OpenAI, and DeepSeek
Ready-to-use Modelfile generation for local deployment in Ollama
Optimized training templates for GLM-4-9B and other agent-centric models
1 GitHub stars
Memory-efficient QLoRA finetuning using Unsloth to fit 9B models in consumer VRAM

사용 사례

Training lightweight models to handle complex inter-agent communication protocols without prompt overhead
Developing high-performance, cost-effective local LLMs for private agentic workflows
Standardizing model outputs to strictly follow the SLIP wire format for system interoperability