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

f9ec6ba

verified ·

1 Parent(s): 7a4684d

Update README.md

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README.md +8 -7

README.md CHANGED Viewed

@@ -185,8 +185,9 @@ The model was trained on a comprehensive dataset of Python code review examples
 - Error Handling and Logging
 ## Training Procedure
 ### Training Hyperparameters
-- Training regime: bf16 mixed precision with QLoRA
 - Base Model: Qwen2.5-7B-Instruct
 - LoRA Rank: 32
 - LoRA Alpha: 64
@@ -209,9 +210,9 @@ Testing Data
 Evaluation performed on held-out Python code examples from the same dataset distribution.
 ### Metrics
-ROUGE-L: 0.754
-BLEU: 61.99
-Validation Loss: 0.595
 ## Results
 The model achieved strong performance on code review tasks, particularly excelling at:
@@ -253,12 +254,12 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
   publisher    = {Hugging Face}
 }
 ## Model Card Authors
-- Alen Philip George
 ## Model Card Contact
-Hugging Face: alenphilip
 LinkedIn: [alenphilipgeorge](linkedin.com/in/alen-philip-george-130226254)
-Email: alenphilipgeorge@gmail.com
 For questions about this model, please use the Hugging Face model repository discussions or contact via the above channels.

 - Error Handling and Logging
 ## Training Procedure
+[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alenphilip2071-google/huggingface/runs/d27nrifd)
 ### Training Hyperparameters
+- Training regime: bf16 mixed precision with SFT & QLoRA
 - Base Model: Qwen2.5-7B-Instruct
 - LoRA Rank: 32
 - LoRA Alpha: 64
 Evaluation performed on held-out Python code examples from the same dataset distribution.
 ### Metrics
+ROUGE-L: 0.754
+BLEU: 61.99
+Validation Loss: 0.595
 ## Results
 The model achieved strong performance on code review tasks, particularly excelling at:
   publisher    = {Hugging Face}
 }
 ## Model Card Authors
+Alen Philip George
 ## Model Card Contact
+Hugging Face: [alenphilip](https://huggingface.co/alenphilip)
 LinkedIn: [alenphilipgeorge](linkedin.com/in/alen-philip-george-130226254)
+Email: [alenphilipgeorge@gmail.com](mailto:alenphilipgeorge@gmail.com)
 For questions about this model, please use the Hugging Face model repository discussions or contact via the above channels.