Instructions to use SmuFypTeam5/GradingQlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmuFypTeam5/GradingQlora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B")
model = PeftModel.from_pretrained(base_model, "SmuFypTeam5/GradingQlora")

Transformers

How to use SmuFypTeam5/GradingQlora with Transformers:

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

pipe = pipeline("text-generation", model="SmuFypTeam5/GradingQlora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SmuFypTeam5/GradingQlora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SmuFypTeam5/GradingQlora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SmuFypTeam5/GradingQlora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SmuFypTeam5/GradingQlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SmuFypTeam5/GradingQlora

SGLang

How to use SmuFypTeam5/GradingQlora 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 "SmuFypTeam5/GradingQlora" \
    --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": "SmuFypTeam5/GradingQlora",
		"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 "SmuFypTeam5/GradingQlora" \
        --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": "SmuFypTeam5/GradingQlora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SmuFypTeam5/GradingQlora with Docker Model Runner:
```
docker model run hf.co/SmuFypTeam5/GradingQlora
```

GradingQlora / test_metrics.csv

awork2103

Upload evaluated Qwen3.5-9B QLoRA +in_proj_qkv +out_proj r=48 alpha=96 seed=42 adapter

a8d4260 verified 3 days ago

Raw History Blame Contribute Delete

551 Bytes

model,examples,valid_examples,recovered_output_rate,invalid_output_rate,exact_match_accuracy,per_criterion_accuracy,precision,recall,f1,final_score_exact_accuracy,final_score_within_one_accuracy,final_score_mae,final_score_rmse,final_score_pearson,final_score_qwk,seed,rank,alpha
"QLoRA r=48, alpha=96",389,387,0.0,0.005141388174807198,0.7840616966580977,0.8970099667774086,0.8622540250447227,0.9678714859437751,0.9120151371807,0.7892030848329049,0.9794344473007712,0.22997416020671835,0.554521000550637,0.8759959382848732,0.8626394767218161,42,48,96

	model,examples,valid_examples,recovered_output_rate,invalid_output_rate,exact_match_accuracy,per_criterion_accuracy,precision,recall,f1,final_score_exact_accuracy,final_score_within_one_accuracy,final_score_mae,final_score_rmse,final_score_pearson,final_score_qwk,seed,rank,alpha
	"QLoRA r=48, alpha=96",389,387,0.0,0.005141388174807198,0.7840616966580977,0.8970099667774086,0.8622540250447227,0.9678714859437751,0.9120151371807,0.7892030848329049,0.9794344473007712,0.22997416020671835,0.554521000550637,0.8759959382848732,0.8626394767218161,42,48,96