Instructions to use Sharpener9290/hcup-llm-humanizer-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sharpener9290/hcup-llm-humanizer-v1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/george/huggingface_models/Mistral-7B-v0.3")
model = PeftModel.from_pretrained(base_model, "Sharpener9290/hcup-llm-humanizer-v1")

Transformers

How to use Sharpener9290/hcup-llm-humanizer-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Sharpener9290/hcup-llm-humanizer-v1")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Sharpener9290/hcup-llm-humanizer-v1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Sharpener9290/hcup-llm-humanizer-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sharpener9290/hcup-llm-humanizer-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sharpener9290/hcup-llm-humanizer-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sharpener9290/hcup-llm-humanizer-v1

SGLang

How to use Sharpener9290/hcup-llm-humanizer-v1 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 "Sharpener9290/hcup-llm-humanizer-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sharpener9290/hcup-llm-humanizer-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Sharpener9290/hcup-llm-humanizer-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sharpener9290/hcup-llm-humanizer-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sharpener9290/hcup-llm-humanizer-v1 with Docker Model Runner:
```
docker model run hf.co/Sharpener9290/hcup-llm-humanizer-v1
```

Model Card for HCUP LLM Humanizer Adapter

Model Details

Base Model: mistralai/Mistral-7B-v0.3
Adapter Type: LoRA (QLoRA 4-bit)
Training: DPO on clinical writing humanization pairs

Purpose

Trained to produce humanized, non-AI-sounding clinical manuscripts using HCUP administrative data patterns.

Usage

from peft import PeftModel, AutoModelForCausalLM, AutoTokenizer
import torch

base = AutoModelForCausalLM.from_pretrained(
    "mistralai/Mistral-7B-v0.3",
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base, "Sharpener9290/hcup-llm-humanizer-v1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.3")

Training Data

400 instruction pairs from HCUP corpus (nis, neds, trinetx, hcup_general)
50 preference pairs for DPO humanization style transfer

LoRA Config

rank=16, alpha=32
target_modules: q_proj, v_proj
dropout=0.05

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