Instructions to use nabinwell/deepseek-react-reviewer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nabinwell/deepseek-react-reviewer with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nabinwell/deepseek-react-reviewer")
model = AutoModelForCausalLM.from_pretrained("nabinwell/deepseek-react-reviewer")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nabinwell/deepseek-react-reviewer with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/nabinwell/deepseek-react-reviewer

SGLang

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

Unsloth Studio new

How to use nabinwell/deepseek-react-reviewer with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nabinwell/deepseek-react-reviewer to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for nabinwell/deepseek-react-reviewer to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nabinwell/deepseek-react-reviewer to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="nabinwell/deepseek-react-reviewer",
    max_seq_length=2048,
)

Docker Model Runner
How to use nabinwell/deepseek-react-reviewer with Docker Model Runner:
```
docker model run hf.co/nabinwell/deepseek-react-reviewer
```

DeepSeek React Reviewer

A fine-tuned DeepSeek-Coder model focused on reviewing React and JavaScript code, identifying issues, and suggesting best practices.

This model is optimized to act as a code reviewer, not just a code generator.

Base Model

DeepSeek-Coder (deepseek-ai/deepseek-coder)
Architecture: Causal Language Model

Fine-Tuning

Method: LoRA (Low-Rank Adaptation)
Framework: Unsloth + Transformers
Training: Supervised fine-tuning (SFT)
Precision: FP16 (LoRA merged)
Training quantization: 4-bit (QLoRA-style)

The model uploaded here is fully merged, so it can be used directly for inference without PEFT dependencies.

Training Data

Format: Chat-style JSONL
Focus areas:
- React code review
- JavaScript / JSX patterns
- Hooks misuse
- Common bugs and anti-patterns
- Performance and best practices

⚠️ The dataset may contain synthetic or curated examples and should not be treated as authoritative.

Intended Use

Recommended for:

Reviewing React and JavaScript code
Explaining bugs and anti-patterns
Suggesting improvements and best practices
Educational feedback for frontend developers

Not intended for:

Security audits
Production-critical code review
Executing untrusted code

Author

Fine-tuned by Nabin Raj Pandey

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

BF16

nabinwell
/

deepseek-react-reviewer