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

Libraries

How to use alphanozcan/essAi with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alphanozcan/essAi")
model = AutoModelForCausalLM.from_pretrained("alphanozcan/essAi", device_map="auto")
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 Settings

vLLM

How to use alphanozcan/essAi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/alphanozcan/essAi

SGLang

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

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

essAi

essAi is a fine-tuned Qwen3-4B model that writes authentic college application essays (Common App personal statement style) in a natural human voice.

Training

Two-stage fine-tune on ~19.7k human-written essays:

Stage	Data	Details
SFT	270 real admissions essays from publicly published example collections (JHU "Essays That Worked", College Essay Guy, AP Study Notes) + ~19.4k human essays from the open persuade corpus	LoRA r=16 (all linear), lr 2e-4, 1 epoch, fp16
DPO	Same prompt: real human essay = chosen, SFT model output = rejected (HumanLLMs method, arXiv 2501.05032) + GradGPT quality pairs	beta=0.1, lr 5e-5, 1 epoch

Prompt from SFT data: Write a ~650-word Common App style personal statement essay. …

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("alphanozcan/essAi", torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("alphanozcan/essAi")

system = "You write authentic college application essays in a natural human voice, with specific personal detail, varied sentence rhythm, and honest reflection."
user = "Write a ~650-word Common App style personal statement essay about learning from failure."

prompt = tok.apply_chat_template(
    [{"role": "system", "content": system}, {"role": "user", "content": user}],
    tokenize=False, add_generation_prompt=True, enable_thinking=False,
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=900, do_sample=True, temperature=0.8, top_p=0.95, pad_token_id=tok.pad_token_id or tok.eos_token_id)
print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

A 4-bit MLX build for Apple Silicon is available at alphanozcan/essAi-mlx.

Notes

4B parameters, 1 training epoch — quality reflects that; longer training and a larger base would improve coherence.
Style metrics (human reference vs output): burstiness CV 0.544 → 0.465, mean sentence length 18.8 → 17.0.
AI-detector behavior is not guaranteed; this model is trained on human essays for a more natural writing style, but detectors are trained classifiers and results vary.

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Safetensors

Model size

4B params

Tensor type

F16

Model tree for alphanozcan/essAi

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Adapter

(1129)

this model