This is the merged version -- LoRA weights have been folded into the base model so it can be served directly without adapter loading. Use this when your inference framework doesn't support the adapter's target modules at runtime (e.g., SGLang with DeltaNet/MoE gate layers).

For the LoRA adapter only, see JinnP/Qwen3.5-397B-A17B-LoRA-SFT-v4.

What Changed in v4

v4 fixes a critical data pipeline bug: v2/v3 silently dropped 66% of training data due to broken role alternation in bookend and solution_chunk views. v4 also introduces the first leak-free evaluation on held-out tasks.

What	v3	v4
Effective training examples	~100 (66% silently dropped)	270 (all 3 views working)
Eval integrity	Leaked (eval = training data)	Clean (10 held-out tasks, zero overlap)
Eval loss	0.044 (meaningless)	0.055 (real generalization)
Train loss	0.059	0.199 (higher: bookend/chunk views are harder)
Best eval checkpoint	n/a	Epoch 8 (0.0547)

Training Details

Parameter	Value
Base model	Qwen/Qwen3.5-397B-A17B (MoE, 17B active)
Hardware	8x AMD Instinct MI355X (ROCm 7.2)
LoRA rank / alpha	32 / 64
Target modules	all (13 types incl. DeltaNet + MoE gate)
Trainable params	128.5M / 396.9B (0.032%)
Dataset	270 examples (3-view from 92 train trajectories, 10 held out for eval)
Cutoff length	32,768 tokens
Epochs / Steps	10 / 200
Batch size	8 (1 per device x 8 GPUs)
Learning rate	2e-5 (cosine schedule)
Weight decay	0.01
Training time	7h 59min
Merge method	LLaMA-Factory `export` (PEFT merge_and_unload on CPU)
Framework	LLaMA-Factory + DeepSpeed ZeRO-3 + PEFT 0.18.1

Usage

Serve with SGLang

bash

python3 -m sglang.launch_server \
    --model-path JinnP/Qwen3.5-397B-A17B-LoRA-SFT-v4-merged \
    --served-model-name Qwen3.5-397B-A17B-SFT-v4 \
    --tp 8 \
    --trust-remote-code \
    --attention-backend triton \
    --mem-fraction-static 0.80 \
    --reasoning-parser qwen3 \
    --tool-call-parser qwen3_coder \
    --host 0.0.0.0 --port 30000

Serve with vLLM

bash

python -m vllm.entrypoints.openai.api_server \
    --model JinnP/Qwen3.5-397B-A17B-LoRA-SFT-v4-merged \
    --tensor-parallel-size 8 \
    --trust-remote-code

Load with Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("JinnP/Qwen3.5-397B-A17B-LoRA-SFT-v4-merged")
model = AutoModelForCausalLM.from_pretrained(
    "JinnP/Qwen3.5-397B-A17B-LoRA-SFT-v4-merged",
    device_map="auto",
    torch_dtype="bfloat16",
)

Dataset

JinnP/amdpilot-lora-sft-dataset -- 102 multi-turn agent trajectories processed into 270 training examples using 3-view extraction (bookend + full + solution chunks). 10 trajectories held out for leak-free evaluation.

Framework Versions

PEFT 0.18.1
Transformers 5.2.0
PyTorch 2.9.1+rocm7.2.0
Datasets 4.0.0
Tokenizers 0.22.2