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

08a3145

verified ·

1 Parent(s): f782870

Update README.md

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@@ -15,6 +15,16 @@ tags:
 - python-security
 - code-optimization
 pipeline_tag: text-generation
 ---
 # Code Review Assistant Model
@@ -245,6 +255,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 - QLoRA for parameter-efficient fine-tuning
 ## Citation
 @misc{alen_philip_george_2025,
   author       = {Alen Philip George},
   title        = {Code_Review_Assistant_Model (Revision 233d438)},
@@ -253,6 +264,7 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
   doi          = {10.57967/hf/6836},
   publisher    = {Hugging Face}
 }
 ## Model Card Authors
 Alen Philip George

 - python-security
 - code-optimization
 pipeline_tag: text-generation
+datasets:
+- alenphilip/Code-Review-Assistant
+- alenphilip/Code-Review-Assistant-Eval
+language:
+- en
+metrics:
+- rouge
+- bleu
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
 ---
 # Code Review Assistant Model
 - QLoRA for parameter-efficient fine-tuning
 ## Citation
+```bibtex
 @misc{alen_philip_george_2025,
   author       = {Alen Philip George},
   title        = {Code_Review_Assistant_Model (Revision 233d438)},
   doi          = {10.57967/hf/6836},
   publisher    = {Hugging Face}
 }
+```
 ## Model Card Authors
 Alen Philip George