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

Libraries

How to use rishanthrajendhran/ProseLens with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rishanthrajendhran/ProseLens")
model = AutoModelForCausalLM.from_pretrained("rishanthrajendhran/ProseLens", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use rishanthrajendhran/ProseLens with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rishanthrajendhran/ProseLens

SGLang

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

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

ProseLens

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{
 "model": "rishanthrajendhran/ProseLens",
 "input": "document",
 "flag_rule": "flag the document as AI when P(human) < cut",
 "calibration": {
  "data": "WildOutlines, calibration split (human documents only)",
  "scores_from": "the training checkpoint, scored on Tinker",
  "n_humans": 80000,
  "n_per_format": {
   "Academic Writing": 10000,
   "Creative Writing": 10000,
   "Knowledge Article": 10000,
   "News Article": 10000,
   "Nonfiction Writing": 10000,
   "Personal About Page": 10000,
   "Personal Blog": 10000,
   "User Reviews": 10000
  }
 },
 "method": {
  "global": "the target-FPR quantile of P(human) over all calibration humans",
  "per_group": "the group's own quantile, shrunk toward the global cut with weight n / (n + 2500)",
  "min_group_humans": 200,
  "estimability": "a cut at FPR q is given only when q * n >= 25 calibration humans",
  "missing_cut": "no cut for a group means none could be estimated; do not substitute the global cut"
 },
 "fpr_targets": [
  0.001,
  0.005,
  0.01,
  0.02,
  0.05
 ],
 "global": {
  "0.001": 0.07081838771272196,
  "0.005": 0.30468918373143306,
  "0.01": 0.6053894506737609,
  "0.02": 0.925022270529183,
  "0.05": 0.998888189099037
 },
 "per_format": {
  "0.005": {
   "Academic Writing": 0.7778763017339165,
   "Creative Writing": 0.8492284331725566,
   "Knowledge Article": 0.24235805929603155,
   "News Article": 0.2955419059997487,
   "Nonfiction Writing": 0.14387439727844545,
   "Personal About Page": 0.4169940435489798,
   "Personal Blog": 0.6827533426351194,
   "User Reviews": 0.6213508153913166
  },
  "0.01": {
   "Academic Writing": 0.8996738533714292,
   "Creative Writing": 0.9174860344190486,
   "Knowledge Article": 0.3804974222436696,
   "News Article": 0.555354294854766,
   "Nonfiction Writing": 0.27050169337460295,
   "Personal About Page": 0.7871415559941629,
   "Personal Blog": 0.8799318831045015,
   "User Reviews": 0.8516071157574004
  },
  "0.02": {
   "Academic Writing": 0.9827934935442895,
   "Creative Writing": 0.9841078402345083,
   "Knowledge Article": 0.6404405355320586,
   "News Article": 0.9037969402135294,
   "Nonfiction Writing": 0.494985268248519,
   "Personal About Page": 0.9772992347199888,
   "Personal Blog": 0.9816307917983351,
   "User Reviews": 0.9803463940922453
  },
  "0.05": {
   "Academic Writing": 0.9996898375219624,
   "Creative Writing": 0.9995782080959261,
   "Knowledge Article": 0.952949484412262,
   "News Article": 0.997881027494693,
   "Nonfiction Writing": 0.9393488591865028,
   "Personal About Page": 0.9996622303035176,
   "Personal Blog": 0.999661233340514,
   "User Reviews": 0.9996383495931975
  }
 }
}