Instructions to use ryomac/lora_sft_full_ep2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryomac/lora_sft_full_ep2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "ryomac/lora_sft_full_ep2")

Transformers

How to use ryomac/lora_sft_full_ep2 with Transformers:

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

pipe = pipeline("text-generation", model="ryomac/lora_sft_full_ep2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ryomac/lora_sft_full_ep2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ryomac/lora_sft_full_ep2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ryomac/lora_sft_full_ep2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ryomac/lora_sft_full_ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryomac/lora_sft_full_ep2

SGLang

How to use ryomac/lora_sft_full_ep2 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 "ryomac/lora_sft_full_ep2" \
    --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": "ryomac/lora_sft_full_ep2",
		"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 "ryomac/lora_sft_full_ep2" \
        --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": "ryomac/lora_sft_full_ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryomac/lora_sft_full_ep2 with Docker Model Runner:
```
docker model run hf.co/ryomac/lora_sft_full_ep2
```

Model Card for `ryomac/lora_sft_full_ep2`

Model Details

Model type: LoRA adapter (PEFT)
Base model: Qwen/Qwen3-4B-Instruct-2507
Goal: Structured output performance improvement for JSON-style competition prompts
Author: ryomac

Intended Use

This repository contains adapter weights only.
Use together with the base model for inference or continued fine-tuning.
Suitable for controlled structured generation tasks.

Out-of-Scope Use

Medical/legal/financial high-stakes decisions.
Fully automated decision pipelines without human validation.

Training Data

Main SFT dataset: u-10bei/structured_data_with_cot_dataset_512_v2
Additional local preprocessing/filtering was applied for training.
Public evaluation set was handled as evaluation/inference target.

Training Procedure

Method: Supervised Fine-Tuning (SFT) with LoRA
Run profile: full training variant (full_ep2)
Precision: fp16 (Mac MPS environment)

Evaluation

Evaluated with local competition-style inference/evaluation workflow.
Performance varies with prompt template and decoding parameters.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3-4B-Instruct-2507"
adapter_id = "ryomac/lora_sft_full_ep2"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base_model = AutoModelForCausalLM.from_pretrained(base_id)
model = PeftModel.from_pretrained(base_model, adapter_id)

Limitations

JSON consistency can degrade on unseen prompt distributions.
Output quality depends on decoding settings and prompt design.
Task-specific validation is required before production usage.

Contact

For reproducibility details, refer to training/inference scripts under scripts/ in this project.

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