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
metadata
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, plainllm_querysubcalls 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.