Instructions to use snowfly/glm3-QLoRA-adgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use snowfly/glm3-QLoRA-adgen with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="snowfly/glm3-QLoRA-adgen", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("snowfly/glm3-QLoRA-adgen", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use snowfly/glm3-QLoRA-adgen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "snowfly/glm3-QLoRA-adgen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "snowfly/glm3-QLoRA-adgen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/snowfly/glm3-QLoRA-adgen

SGLang

How to use snowfly/glm3-QLoRA-adgen with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "snowfly/glm3-QLoRA-adgen" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "snowfly/glm3-QLoRA-adgen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "snowfly/glm3-QLoRA-adgen" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "snowfly/glm3-QLoRA-adgen",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use snowfly/glm3-QLoRA-adgen with Docker Model Runner:
```
docker model run hf.co/snowfly/glm3-QLoRA-adgen
```

模型介绍

在ChatGLM3-6B模型上使用QLoRA在HasturOfficial/adgen数据集上进行广告生成微调

数据集介绍

HasturOfficial/adgen是广告生成数据集

微调相关设置

微调使用一张4090显卡
使用nf4量化数据类型加载模型，开启双量化配置，以bf16混合精度训练
per_device_train_batch_size = 8
gradient_accumulation_steps = 4
learning_rate = 1e-3
warmup_ratio=0.1
lr_scheduler_type="linear"
lora_rank = 4
lora_alpha = 32
lora_dropout = 0.05

使用方法

from transformers import AutoTokenizer, AutoModel

model_name_or_path = "snowfly/glm3-QLoRA-adgen"
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name_or_path, trust_remote_code=True)
model = AutoModel.from_pretrained(pretrained_model_name_or_path=model_name_or_path, trust_remote_code=True, device='cuda')
model = model.eval()
input_text = '类型#裙*版型#显瘦*风格#文艺*风格#简约*图案#印花*图案#撞色*裙下摆#压褶*裙长#连衣裙*裙领型#圆领'
response, history = model.chat(tokenizer=tokenizer, query=input_text, history=[])
print(response)

Downloads last month: 3

Safetensors

Model size

6B params

Tensor type

F16

snowfly
/

glm3-QLoRA-adgen

模型介绍

数据集介绍

微调相关设置

使用方法

Dataset used to train snowfly/glm3-QLoRA-adgen