This is the PEFT LoRA adapter from the Genesis-1.0 SFT training run. It was fine-tuned on 20K tool-use demonstrations (9 curated datasets) for 1 epoch (1,187 steps) using QLoRA.

Details

Parameter	Value
Base model	`Qwen/Qwen3.6-35B-A3B`
Adapter rank	64
Adapter alpha	128
Target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj` (attention only)
Training data	20K tool-use trajectories
Training steps	1,187 (1 epoch)
Final loss	~0.65
Precision	BF16 (base quantized to NF4 via QLoRA)

Files

File	Description
`adapter_model.safetensors`	LoRA weights (55 MB)
`adapter_config.json`	PEFT LoRA configuration
`checkpoint-1187/`	Full trainer checkpoint with optimizer/scheduler states
`config.json`	Model configuration
`tokenizer.json` / `tokenizer_config.json`	Tokenizer files

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen3.6-35B-A3B",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

model = PeftModel.from_pretrained(
    base_model,
    "jacobeen06/Genesis-1.0-SFT-adapter",
)

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.6-35B-A3B")

Next Steps

This adapter is the starting point for RL post-training:

DPO — align tool-choice preferences
GRPO — reinforcement learning with verifiable rewards
All-linear LoRA is recommended for RL phases (expand target modules)

jacobeen06/Genesis-1.0-SFT-adapter

Genesis-1.0 SFT Adapter

Details

Files

Usage

Next Steps

Links

jacobeen06/
Genesis-1.0-SFT-adapter