Instructions to use ryomac/lora_sft_v2_weak650_ep2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ryomac/lora_sft_v2_weak650_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_v2_weak650_ep2")

Transformers

How to use ryomac/lora_sft_v2_weak650_ep2 with Transformers:

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

pipe = pipeline("text-generation", model="ryomac/lora_sft_v2_weak650_ep2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ryomac/lora_sft_v2_weak650_ep2", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ryomac/lora_sft_v2_weak650_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_v2_weak650_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_v2_weak650_ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ryomac/lora_sft_v2_weak650_ep2

SGLang

How to use ryomac/lora_sft_v2_weak650_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_v2_weak650_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_v2_weak650_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_v2_weak650_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_v2_weak650_ep2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ryomac/lora_sft_v2_weak650_ep2 with Docker Model Runner:
```
docker model run hf.co/ryomac/lora_sft_v2_weak650_ep2
```

Model Card for `ryomac/lora_sft_v2_weak650_ep2`

Model Details

Model type: LoRA adapter (PEFT) for causal language modeling
Base model: Qwen/Qwen3-4B-Instruct-2507
Purpose: Structured output tuning for JSON-style answer formatting in competition-style prompts
Developer: ryomac

Intended Use

This repository provides adapter weights only (adapter_model.safetensors).
Load this adapter on top of the base model for inference/fine-tuning.
Recommended use: controlled generation tasks requiring stable structured responses.

Out-of-Scope Use

High-stakes decision making (medical/legal/financial).
Safety-critical or fully autonomous production workflows without human review.

Training Data

Main SFT source: u-10bei/structured_data_with_cot_dataset_512_v2
Additional local preprocessing/filtering was applied for training convenience.
Evaluation/public benchmark files were treated as inference/evaluation targets only.

Training Procedure

Method: Supervised Fine-Tuning (SFT) with LoRA
Typical settings used in this run:
- Epochs: 2
- Learning rate: low-range (competition tuning)
- LoRA rank/alpha: medium-to-high rank setup
- Mixed precision: fp16 (MPS environment)

Evaluation

This adapter was validated in local competition-style evaluation.
Reported score reference exists in project documentation (docs/report_v2_weak650_ep2.md).
Results can vary by prompt template, decoding params, and inference implementation.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_id = "Qwen/Qwen3-4B-Instruct-2507"
adapter_id = "ryomac/lora_sft_v2_weak650_ep2"

tok = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(base_id)
model = PeftModel.from_pretrained(base, adapter_id)

Limitations

Structured output quality is sensitive to prompt format and decoding settings.
May produce malformed JSON or schema mismatches on out-of-distribution tasks.
Performance claims are task-specific and should be independently verified.

Contact

For reproducibility context, refer to this project repository and training scripts under scripts/.

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