metadata
base_model: Qwen/Qwen2.5-Coder-14B-Instruct
library_name: mlx
pipeline_tag: text-generation
tags:
- base_model:adapter:Qwen/Qwen2.5-Coder-14B-Instruct
- mlx
- mlx-lm
- lora
- frontend
- agent
- planner
- zh
- en
language:
- zh
- en
FrontAgent Planner 14B LoRA MLX
This repository contains a FrontAgent Planner-only LoRA adapter trained with MLX-LM on Apple Silicon. It generates structured frontend engineering execution plans for FrontAgent. It does not directly generate business code, run tools, or make repository changes. Generated plans still require FrontAgent schema validation and runtime safety checks.
Artifact
- Adapter format: MLX-LM LoRA
- Adapter weights:
adapters.safetensors - Recommended MLX base:
mlx-community/Qwen2.5-Coder-14B-Instruct-4bit - Upstream base model:
Qwen/Qwen2.5-Coder-14B-Instruct
Training Summary
- Data: 500 v2 train samples and 100 v2 eval samples
- MLX split: 500 train, 80 valid, 20 test
- Total training: 1000 iterations (50-iter stable pilot + 950 continuation iterations)
- Learning rate: 1e-5
- LoRA: all layers, rank 32, alpha/scale 64
- Max sequence length: 2048
- Peak memory observed on local Mac: 13.249 GB
- Final test loss: 0.000
- Final test perplexity: 1.000
Usage with MLX-LM
python -m venv .venv
.venv/bin/python -m pip install mlx-lm
.venv/bin/python -m mlx_lm generate \
--model mlx-community/Qwen2.5-Coder-14B-Instruct-4bit \
--adapter-path ceilf6/frontagent-planner-14B-lora \
--system-prompt '你是一个资深前端工程师和项目规划专家。只输出 JSON，不要输出 markdown。' \
--prompt '任务：创建一个用户登录页面，包含邮箱和密码输入框，支持表单验证\n项目上下文：React 18 + TypeScript + Ant Design 5' \
--max-tokens 1536 \
--temp 0
Limitations
- This adapter is MLX-LM format, not a PEFT/Transformers adapter.
- It is optimized for FrontAgent planning JSON, not general coding.
- Generated plans must be schema-checked before execution.
- Loss metrics are from the deterministic v2 dataset split; run FrontAgent generated-plan quality evaluation before production rollout.