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glm4v-sft-lora – AI Model by HUNGTZE | AlphaNeural AI

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HUNGTZE/
glm4v-sft-lora

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GLM-4.6V SFT LoRA

這是基於 GLM-4.6V 108B MoE 模型進行 SFT (Supervised Fine-Tuning) 訓練的 LoRA adapter。

模型資訊

Base Model: GLM-4.6V 108B MoE (128 experts, 8 active)
Training Method: QLoRA (4-bit quantization + LoRA)
LoRA Rank: 64
LoRA Alpha: 128
Training Steps: 600
Max Sequence Length: 4096

訓練配置

Hardware: 4x NVIDIA H100 80GB
Precision: BF16
Optimizer: AdamW
Learning Rate: 2e-5
Batch Size: 1 per GPU
Gradient Accumulation: 8

使用方式

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

# 載入基礎模型
base_model = AutoModelForCausalLM.from_pretrained(
    "your-base-model-path",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# 載入 LoRA adapter
model = PeftModel.from_pretrained(base_model, "HUNGTZE/glm4v-sft-lora")

# 載入 tokenizer
tokenizer = AutoTokenizer.from_pretrained("HUNGTZE/glm4v-sft-lora")

Framework Versions

PEFT: 0.18.0
Transformers: 4.x
PyTorch: 2.x