Qwen3-4B-MegaR3ASONER-LoRA-v1

Name: Qwen3-4B-MegaR3ASONER-LoRA-v1
Availability: InStock

Reasoning-focused PEFT LoRA adapter for Qwen/Qwen3-4B-Thinking-2507.

Important

This repository contains LoRA adapter weights only. Load it on the exact base checkpoint:

Qwen/Qwen3-4B-Thinking-2507

Do not attach it to Qwen/Qwen3-4B-Instruct-2507 or another base model.

Training overview

Method: BF16 16-bit LoRA
Hardware: NVIDIA A10G on Modal
Focus: mathematical, scientific, coding, and general reasoning
Training sources:
- HuggingFaceH4/Bespoke-Stratos-17k
- nvidia/OpenMathReasoning
- nvidia/OpenCodeReasoning
- nvidia/OpenScienceReasoning-2
- lordx64/reasoning-distill-opus-4-7-max-sft

Preliminary evaluation

On the first 100 examples selected by EleutherAI's gsm8k_cot task:

Flexible extraction exact match: 88%
Strict match: 83%

This was a limited 100-question run, not the complete GSM8K test set and not a controlled base-versus-adapter comparison.

Loading

python

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Qwen/Qwen3-4B-Thinking-2507"
adapter_id = "RexTRO111/Qwen3-4B-MegaR3ASONER-LoRA-v1"

tokenizer = AutoTokenizer.from_pretrained(base_id)

base = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

model = PeftModel.from_pretrained(
    base,
    adapter_id,
    is_trainable=False,
)
model.eval()

Limitations

The model may produce incorrect intermediate reasoning even when its final answer is correct.
It can overthink simple prompts.
Long reasoning traces increase latency.
Benchmark contamination has not been exhaustively ruled out.
Verify outputs before high-stakes use.

Licensing note

The base model and each training dataset retain their own licenses and upstream terms. Review all of them before redistribution or commercial use.