benchflow/benchflow-qwen35-9b

BenchFlow Qwen3.5-9B Env-0 Qwen397B-Data Custom SFT LoRA Adapter

This repository contains the current BenchFlow env-0 SFT adapter for Qwen/Qwen3.5-9B. It is a PEFT LoRA adapter only; load it with the base Qwen/Qwen3.5-9B checkpoint.

Current Version

Field	Value
Adapter repo	benchflow/benchflow-qwen35-9b
Published model PR	HF PR #4
Adapter commit promoted from PR	92380a83764ec2d8b2103a3895e24e49a508d1d9
Training run id	qwen35-397b-data-qwen35-9b-custom-sft-20260630T042600Z
Base checkpoint	Qwen/Qwen3.5-9B
Adapter type	LoRA / PEFT
Trainer	Custom PyTorch + PEFT LoRA trainer, experiments/env-0-posttrain-mvp/train_lora_sft.py
Source data	Qwen3.5-397B teacher trajectories collected with BenchFlow, OpenHands, and Daytona
Training rows	298 all-training-ready rows
Hardware	1x H100 80GB

This run used the historical custom trainer. Prime-RL was not used as the SFT trainer; the source data path includes prime-rl only because the trajectories were also validated and exported in Prime-SFT-compatible format.

Training Recipe

Field	Value
Precision	BF16
Quantization	None
Context length	8192
Max trainer steps	300 micro-batch steps
Micro batch size	1
Gradient accumulation	8
Approx optimizer updates	37
Learning rate	1e-4
Scheduler	None
Max grad norm	1.0
LoRA rank	32
LoRA alpha	64
LoRA dropout	0.05
LoRA targets	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Checkpoints	100, 200, 300, best_adapter, final_adapter
Final eval loss	0.1476329118013382

Evaluation Results

Evaluation	Runtime	Strict pass
Mobile300	SGLang	135 / 300
Mobile300	Fireworks	134 / 300
standard60, 3 trials	Fireworks	25 / 180

Artifact Links

Artifact	Link
Source teacher trajectories	HF dataset folder
Training artifacts	HF dataset folder
Fireworks Mobile300 eval	HF dataset folder
Fireworks standard60 eval	HF dataset folder
Reproduction report	GitHub report

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3.5-9B"
adapter_id = "benchflow/benchflow-qwen35-9b"

tokenizer = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_id)

Intended Use And Limitations

This adapter is an env-0 research artifact for controlled BenchFlow/OpenHands/Daytona evaluation. It is not a general-purpose safety-tested assistant model and should not be treated as production-ready for autonomous operation.