Qwen3-8B SDFT checkpoint with a search-R1 RL LoRA adapter merged into the base weights (full dense model, not an adapter — load it like any Qwen3-8B).

What this is


Base	Qwen3-8B-Base, SDFT'd on an oracle-mix (`Qwen3-8B-oracle-mix-SFT-balance`)
RL	search-R1 style multi-turn retrieval RL, trained with slime (GSPO)
Adapter	LoRA rank 16, alpha 32 (scaling 2.0) on `q/k/v`, `o_proj`, `gate/up_proj`, `down_proj` of all 36 layers
Iteration	RL step 160
Merge	`W += (alpha/r) * (B @ A)` applied in Megatron parameter space, then converted to HF

Provenance / honesty notes

The adapter at this iteration is verified trained: all 144 LoRA modules have non-zero lora_B (max |lora_B| = 1.46e-4). Earlier iterations of this project silently saved untrained adapters (lora_B bit-exactly 0) because a TIS rejection-sampling veto zeroed the gradient for effectively every sequence; those are not published.
The merged delta is small: relative Frobenius norm ||ΔW||/||W|| per LoRA'd matrix is min 5.2e-5, median 9.6e-5, max 2.4e-4. This is "SDFT base + a real but modest RL delta", not a heavily-RL'd model. Treat it accordingly in evaluations.
Verified after merge: 0 non-finite tensors, embedding shape (151936, 4096), greedy decoding sane (The capital of France is -> Paris, 12*8= -> 96).
Tokenizer/config assets come from Qwen3-8B-Base (unchanged).

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "willamazon1/sdft-search-lora-iter160"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="bfloat16", device_map="auto")