Instructions to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-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-Patch-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-Patch-LoRA")

Transformers

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-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-Patch-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-Patch-LoRA", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rohhaiil/SysMLv2-Repair-DeepSeek-Coder-6.7B-Instruct-Patch-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-Patch-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-Patch-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-Patch-LoRA

SGLang

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

rohhaiil commited on 28 days ago

Commit

a92f6fa

verified ·

1 Parent(s): beaec4d

Update README.md

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README.md +16 -31

README.md CHANGED Viewed

@@ -8,6 +8,10 @@ tags:
 - sft
 - transformers
 - trl
 licence: license
 pipeline_tag: text-generation
 ---
@@ -15,25 +19,8 @@ pipeline_tag: text-generation
 # Model Card for patch
 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).
-## Quick start
-```python
-from transformers import pipeline
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
-```
-## Training procedure
-This model was trained with SFT.
 ### Framework versions
@@ -44,19 +31,17 @@ This model was trained with SFT.
 - Datasets: 4.4.2
 - Tokenizers: 0.22.2
-## Citations
-Cite TRL as:
 ```bibtex
-@misc{vonwerra2022trl,
-	title        = {{TRL: Transformer Reinforcement Learning}},
-	author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
-	year         = 2020,
-	journal      = {GitHub repository},
-	publisher    = {GitHub},
-	howpublished = {\url{https://github.com/huggingface/trl}}
 }
 ```

 - sft
 - transformers
 - trl
+- code
+- code-repair
+- sysmlv2
+- patch-generation
 licence: license
 pipeline_tag: text-generation
 ---
 # Model Card for patch
 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).
+It generates diff patches for repairing the code.
 ### Framework versions
 - 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}
 }
 ```