Instructions to use HollowMan6/GLM-5-NOOP-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HollowMan6/GLM-5-NOOP-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5")
model = PeftModel.from_pretrained(base_model, "HollowMan6/GLM-5-NOOP-LoRA")

Transformers

How to use HollowMan6/GLM-5-NOOP-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="HollowMan6/GLM-5-NOOP-LoRA")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("HollowMan6/GLM-5-NOOP-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HollowMan6/GLM-5-NOOP-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HollowMan6/GLM-5-NOOP-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HollowMan6/GLM-5-NOOP-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HollowMan6/GLM-5-NOOP-LoRA

SGLang

How to use HollowMan6/GLM-5-NOOP-LoRA 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 "HollowMan6/GLM-5-NOOP-LoRA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HollowMan6/GLM-5-NOOP-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "HollowMan6/GLM-5-NOOP-LoRA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HollowMan6/GLM-5-NOOP-LoRA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HollowMan6/GLM-5-NOOP-LoRA with Docker Model Runner:
```
docker model run hf.co/HollowMan6/GLM-5-NOOP-LoRA
```

GLM-5 Empty LoRA Adapter (All-Linear + MoE Experts)

Model Summary

This repository contains an empty-initialized PEFT LoRA adapter for zai-org/GLM-5.

It is intended for:

LoRA loading/integration tests
Runtime compatibility checks (PEFT / vLLM)
A clean initialization starting point before actual LoRA training

This adapter is initialized as a near no-op:

lora_A: Kaiming-uniform
lora_B: zeros

So generation quality should be close to the base model before any fine-tuning.

Model Details

Developed by: This script
Model type: PEFT LoRA adapter checkpoint
Base model: zai-org/GLM-5
Language(s): Same as base model
License: Same as base model license
Framework: PEFT

Adapter Construction

This checkpoint was generated programmatically (not fine-tuned from data), targeting:

all linear-like modules (excluding lm_head)
detected MoE expert projections (gate_proj, up_proj, down_proj) and gate when available

Intended Use

Verifying LoRA checkpoint loading
Testing MoE LoRA plumbing
Serving/inference pipeline validation

Out-of-Scope Use

Task performance improvement without training
Benchmark comparisons against fine-tuned adapters

Training Details

No training was performed.
This is an initialization-only adapter checkpoint.

Evaluation

No task evaluation metrics are reported for this adapter.
Expected behavior is close to the base model due to zero-initialized lora_B.

Risks and Limitations

Inherits all limitations and biases of the base model.
Not suitable as a production task adapter without fine-tuning.
Minor output differences may still appear due to runtime/kernel nondeterminism.

Usage

Transformers + PEFT

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = "zai-org/GLM-5"
adapter = "/path/to/this/adapter"

tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base, trust_remote_code=True)
model = PeftModel.from_pretrained(model, adapter)

vLLM

from vllm import LLM, SamplingParams
from vllm.lora.request import LoRARequest

llm = LLM(
    model="zai-org/GLM-5",
    trust_remote_code=True,
    enable_lora=True,
    max_loras=1,
    max_lora_rank=8,  # set >= adapter rank
)

outputs = llm.generate(
    ["Hello!"],
    SamplingParams(temperature=0.0, max_tokens=32),
    lora_request=LoRARequest("empty-lora", 1, "/path/to/this/adapter"),
)
print(outputs[0].outputs[0].text)

Framework Versions

PEFT 0.18.1

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