Instructions to use lsteno/Qwen3-4B-Instruct-2507-RLM-RL-depth1-r4-a8-lr5e-7-s150-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lsteno/Qwen3-4B-Instruct-2507-RLM-RL-depth1-r4-a8-lr5e-7-s150-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507") model = PeftModel.from_pretrained(base_model, "lsteno/Qwen3-4B-Instruct-2507-RLM-RL-depth1-r4-a8-lr5e-7-s150-lora") - Notebooks
- Google Colab
- Kaggle
| base_model: Qwen/Qwen3-4B-Instruct-2507 | |
| library_name: peft | |
| tags: | |
| - peft | |
| - lora | |
| - prime-rl | |
| - rlm-rlvr | |
| - qwen3 | |
| # Qwen3-4B RLM RLVR Depth-1 LoRA Adapter | |
| LoRA adapter from the first rank/LR ablation run. | |
| - Base model: `Qwen/Qwen3-4B-Instruct-2507` | |
| - Run id: `rlm-rlvr-qwen3-4b-depth1-llmonly-r4-a8-lr5e-7-s150` | |
| - Prompt variant: `sanjaya_text_depth1_llm_only_v1` | |
| - Runtime depth: depth-1 LLM-only orchestration (`max_depth = 0`, plain `llm_query` subcalls enabled) | |
| - LoRA rank: 4 | |
| - LoRA alpha: 8 | |
| - Learning rate: 5e-7 | |
| - Training steps: 150 | |
| - Final adapter source: `run_default/broadcasts/step_150` | |
| The `run_configs/` directory contains the exact trainer and orchestrator TOML files saved with the run. | |