Hey r/LocalLLaMA! 👋

Ever struggled with navigating a massive, complex training framework like MS-SWIFT? Trying to figure out the exact CLI arguments for LoRA, or how to implement GRPO training without endlessly digging through documentation?

My team at LocoreMind just open-sourced the solution: LocoTrainer.

This isn't just another general-purpose model; it is a highly specialized system consisting of two parts designed to work perfectly together:

1. The LocoTrainer Framework: A local, Claude Code-style agent loop.
2. LocoTrainer-4B: A 4B-parameter model distilled from Qwen3-Coder-Next, trained specifically to be an MS-SWIFT Domain Expert.

🎯 What does it actually do?

You simply ask it a question about MS-SWIFT (e.g., "How do I use ms-swift to train a model with DPO?" or "What are the default LoRA settings?").

The LocoTrainer-4B model uses its deep framework knowledge combined with multi-turn tool calling (Read, Grep, Glob, Bash, Write) to actively search the MS-SWIFT repository, read the source code, and output a comprehensive, accurate Markdown report.

Because it was trained on 361k+ samples of MS-SWIFT documentation, CLI parameters, and project structures, it answers framework-specific questions accurately without the typical LLM hallucination.

🔗 Links

• Model: LocoreMind/LocoTrainer-4B
• GGUF: LocoreMind/LocoTrainer-4B-GGUF
• GitHub (The Agent Framework): LocoTrainer Repo
• Colab Demo: Jupyter Notebook

📊 Model Specs

• Base: Qwen3-4B-Instruct-2507 (Distilled from Qwen3-Coder-Next)
• Context: 32,768 tokens (Covers 90% of long-context analysis scenarios for this repo)
• Training: Full-parameter SFT on 8x H100s. We trained it to output strictly structured <tool_call> JSON arrays for the framework.

💻 Try it locally (Zero API Cost)

We designed this to run entirely locally on a Mac or modest GPU. When you run it for the first time, our CLI will even automatically clone the ms-swift repo for the agent to analyze.

1. Start the GGUF model via llama.cpp:

./llama-server -m LocoTrainer-4B.gguf --ctx-size 32768 --port 8080 

2. Install the agent framework:

pip install locotrainer 

3. Ask your MS-SWIFT question:

export LOCOTRAINER_BASE_URL=http://localhost:8080/v1 export LOCOTRAINER_MODEL=LocoTrainer-4B export LOCOTRAINER_API_KEY=local # Let the agent do the work: locotrainer run -q "What are all supported training methods in ms-swift and their differences?" 

(The framework injects absolute paths so the model never has to guess, mirroring Claude Code's design. This took our tool-calling reliability from 0% to 100% in tests).

Note: Because it is an MS-SWIFT domain expert (4B params), its performance on completely unrelated codebases is untested. We built this to solve a specific problem perfectly, rather than being mediocre at everything.

We’d love for anyone who uses MS-SWIFT (or just loves local agent loops) to give it a spin! Happy to answer any questions.