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qwen3-4b-agentnet-text-lora
rakesh0x/qwen3-4b-agentnet-text-lora
qwen3-4b-agentnet-text-lora is a machine learning model from rakesh0x. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as mit.
LoRA adapter for mlx-community/Qwen3-4B-4bit, trained on text-only Ubuntu steps from xlangai/AgentNet (agentnetubuntu5k.jsonl). No screenshots were used.
Access
Public
Updated Aug 16, 2026
Repo size
58.8 MB
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Repo makeup
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.safetensors73.5 MB · 100%
At a glance
- Library
- mlx
- License
- mit
- Access
- Public
- Created
- Aug 16, 2026
- Updated
- Aug 16, 2026
- SHA
- f58b1dfe
Base models
Model card
From the Hugging Face model README
Qwen3-4B AgentNet text LoRA (MLX)
LoRA adapter for mlx-community/Qwen3-4B-4bit, trained on text-only Ubuntu steps from xlangai/AgentNet (agentnet_ubuntu_5k.jsonl). No screenshots were used.
This adapter teaches OpenCUA-style Thought / Action / Code (pyautogui) from a written screen observation. It does not add visual grounding.
Training
- Method: MLX LoRA (
mlx_lm.lora), rank 8, 8 layers,mask-prompt - Steps: 400 · batch 1 · lr 1e-5 · max seq 2048
- Data: 2000 AgentNet text steps
- Trainable params: 3.67M (0.091%)
- Val loss: 1.955 → 0.989
- Hardware: Apple M4, peak ~3.9 GB
Use
python3 -m mlx_lm generate \
--model mlx-community/Qwen3-4B-4bit \
--adapter-path rakesh0x/qwen3-4b-agentnet-text-lora \
--prompt "You are a GUI agent. Open LibreOffice Writer. Output Thought, Action, and Code."
from mlx_lm import load, generate
model, tokenizer = load(
"mlx-community/Qwen3-4B-4bit",
adapter_path="rakesh0x/qwen3-4b-agentnet-text-lora",
)
Final weights: adapters.safetensors. Checkpoints 0000100 … 0000400 are intermediate saves.
If you use AgentNet / OpenCUA, please cite Wang et al., 2025.
- Library
- mlx
- License
- mit
- Created
- Aug 16, 2026
- Updated
- Aug 16, 2026