Instructions to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-6.7b-instruct")
model = PeftModel.from_pretrained(base_model, "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA")

Transformers

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA with Transformers:

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

pipe = pipeline("text-generation", model="rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA

SGLang

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-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 "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA" \
    --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": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA",
		"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 "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA" \
        --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": "rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA with Docker Model Runner:
```
docker model run hf.co/rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA
```

SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Code-LoRA / README.md

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	---
	base_model: deepseek-ai/deepseek-coder-6.7b-instruct
	library_name: peft
	model_name: code
	tags:
	- base_model:adapter:deepseek-ai/deepseek-coder-6.7b-instruct
	- lora
	- sft
	- transformers
	- trl
	- code
	- code-repair
	- sysmlv2
	licence: license
	pipeline_tag: text-generation
	---

	This model is a fine-tuned version of [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct).
	It has been trained using [TRL](https://github.com/huggingface/trl) on [this dataset](https://huggingface.co/datasets/rohhaiil/SysMLv2_Repair_with_SLMs).

	### Framework versions

	- PEFT 0.18.0
	- TRL: 0.26.2
	- Transformers: 4.57.3
	- Pytorch: 2.2.2
	- Datasets: 4.4.2
	- Tokenizers: 0.22.2

	## Citation

	GitHub Repository: [SysMLv2 Repair with KG-SLMs](https://github.com/rohailamalik/SysMLv2-repair-with-KG-SLMs)
	```bibtex
	@inproceedings{alshami2026sysml,
	title={Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs},
	author={Al-Shami, Haitham and Malik, Rohail and Ala-Laurinaho, Riku and Veps{\"a}l{\"a}inen, Jari and Viitala, Raine},
	booktitle={Proceedings of the 36th INCOSE International Symposium},
	year={2026},
	address={Yokohama, Japan},
	month={June},
	date={16}
	}
	```