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alphanozcan/essAi-9b

sourceHugging Faceapache-2.0updated 25d agoView on Hugging Face

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essAi 9B

essAi 9B is a fine-tuned Qwen3.5-9B model that writes authentic college application essays (Common App personal statement style) in a natural human voice. It is the larger sibling of alphanozcan/essAi (Qwen3-4B).

Training

Two-stage fine-tune on 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, bf16, 1× A100
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

python

from transformers import AutoTokenizer, AutoModelForCausalLM

try:
    model = AutoModelForCausalLM.from_pretrained("alphanozcan/essAi-9b", torch_dtype="auto", device_map="auto")
except ValueError:
    from transformers import AutoModelForImageTextToText
    model = AutoModelForImageTextToText.from_pretrained("alphanozcan/essAi-9b", torch_dtype="auto", device_map="auto")
tok = AutoTokenizer.from_pretrained("alphanozcan/essAi-9b")

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-9b-mlx.

Notes

—9B parameters, 1 training epoch per stage.
—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.