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

Libraries

How to use alenphilip/Code_Review_Assistant_Model with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alenphilip/Code_Review_Assistant_Model")
model = AutoModelForCausalLM.from_pretrained("alenphilip/Code_Review_Assistant_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use alenphilip/Code_Review_Assistant_Model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alenphilip/Code_Review_Assistant_Model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/alenphilip/Code_Review_Assistant_Model

SGLang

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

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

alenphilip commited on Oct 30, 2025

Commit

6d9d949

verified ·

1 Parent(s): 5459acf

Update README.md

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README.md +21 -1

README.md CHANGED Viewed

@@ -142,7 +142,27 @@ def get_user_by_email(email):
 result = review_python_code(vulnerable_code)
 print(result)
 ```
 # Training Details
 ## Training Data
 The model was trained on a comprehensive dataset of Python code review examples covering:

 result = review_python_code(vulnerable_code)
 print(result)
 ```
+#### OR
+```python
+# Use a pipeline as a high-level helper
+from transformers import pipeline
+pipe = pipeline("text-generation", model="alenphilip/Code_Review_Assistant_Model")
+prompt = "Review this Python code and provide improvements with fixed code:\n\n```python\nclass LockManager:\n    def __init__(self, lock1, lock2):\n        self.lock1 = lock1\n        self.lock2 = lock2\n\n    def acquire_both(self):\n        self.lock1.acquire()\n        self.lock2.acquire() # This might fail\n\n    def release_both(self):\n        self.lock1.release()\n        self.lock2.release()\n```"
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},
+    {"role": "user", "content": prompt},
+]
+result = pipe(messages)
+conversation = result[0]['generated_text']
+for message in conversation:
+    print(f"\n{message['role'].upper()}:")
+    print("-" * 50)
+    print(message['content'])
+    print()
+print("=" * 70)
+```
 # Training Details
 ## Training Data
 The model was trained on a comprehensive dataset of Python code review examples covering: