Text Generation
Transformers
Safetensors
English
nemotron_h
ai-text-detection
idea-provenance
conversational
Instructions to use rishanthrajendhran/IdeaLens-NoParaphrase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/IdeaLens-NoParaphrase with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rishanthrajendhran/IdeaLens-NoParaphrase") 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/IdeaLens-NoParaphrase") model = AutoModelForCausalLM.from_pretrained("rishanthrajendhran/IdeaLens-NoParaphrase", 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/IdeaLens-NoParaphrase with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rishanthrajendhran/IdeaLens-NoParaphrase" # 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/IdeaLens-NoParaphrase", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rishanthrajendhran/IdeaLens-NoParaphrase
- SGLang
How to use rishanthrajendhran/IdeaLens-NoParaphrase 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/IdeaLens-NoParaphrase" \ --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/IdeaLens-NoParaphrase", "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/IdeaLens-NoParaphrase" \ --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/IdeaLens-NoParaphrase", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rishanthrajendhran/IdeaLens-NoParaphrase with Docker Model Runner:
docker model run hf.co/rishanthrajendhran/IdeaLens-NoParaphrase
Download thresholds.json from rishanthrajendhran/IdeaLens-NoParaphrase: direct link, hf CLI and curl.
- Browser
- Download file 5.92 kB
-
https://huggingface.co/rishanthrajendhran/IdeaLens-NoParaphrase/resolve/main/thresholds.json
- Command line
-
hf download hf://rishanthrajendhran/IdeaLens-NoParaphrase/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/rishanthrajendhran/IdeaLens-NoParaphrase/resolve/main/thresholds.json
5.92 kB
| { | |
| "model": "rishanthrajendhran/IdeaLens-NoParaphrase", | |
| "input": "outline", | |
| "flag_rule": "flag the document as AI when P(human) < cut", | |
| "calibration": { | |
| "data": "WildOutlines, calibration split (human documents only)", | |
| "scores_from": "the merged weights released in the IdeaLens-NoParaphrase repo, scored locally in bf16", | |
| "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 | |
| }, | |
| "n_per_topic": { | |
| "Art & Design": 3338, | |
| "Crime & Law": 3931, | |
| "Education & Jobs": 3974, | |
| "Entertainment": 4692, | |
| "Fashion & Beauty": 2999, | |
| "Finance & Business": 4855, | |
| "Food & Dining": 2696, | |
| "Games": 4257, | |
| "Hardware": 2253, | |
| "Health": 5069, | |
| "History": 2554, | |
| "Home & Hobbies": 4192, | |
| "Industrial": 2299, | |
| "Literature": 3571, | |
| "Politics": 3728, | |
| "Religion": 3617, | |
| "Science & Tech.": 4154, | |
| "Social Life": 3126, | |
| "Software": 2257, | |
| "Software Dev.": 2468, | |
| "Sports & Fitness": 4224, | |
| "Transportation": 2810, | |
| "Travel": 2936 | |
| } | |
| }, | |
| "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": 0.005384853958339764, | |
| "0.005": 0.03358698994313534, | |
| "0.01": 0.09947020787557571, | |
| "0.02": 0.5039061705290805, | |
| "0.05": 0.9473810516663606 | |
| }, | |
| "per_format": { | |
| "0.005": { | |
| "Academic Writing": 0.17216314716685993, | |
| "Creative Writing": 0.3662160531627341, | |
| "Knowledge Article": 0.02454634706938552, | |
| "News Article": 0.028123206732030795, | |
| "Nonfiction Writing": 0.016817357478891553, | |
| "Personal About Page": 0.05767136910755699, | |
| "Personal Blog": 0.10742873177223775, | |
| "User Reviews": 0.15260353567912108 | |
| }, | |
| "0.01": { | |
| "Academic Writing": 0.6119850970645432, | |
| "Creative Writing": 0.6022245427590303, | |
| "Knowledge Article": 0.05112801696535962, | |
| "News Article": 0.07551940581028012, | |
| "Nonfiction Writing": 0.04034312052429626, | |
| "Personal About Page": 0.2325741109691407, | |
| "Personal Blog": 0.47574548977669645, | |
| "User Reviews": 0.3578672876227978 | |
| }, | |
| "0.02": { | |
| "Academic Writing": 0.8589840892409853, | |
| "Creative Writing": 0.8079607867126872, | |
| "Knowledge Article": 0.16982042496739946, | |
| "News Article": 0.3361014661410378, | |
| "Nonfiction Writing": 0.14607681811844042, | |
| "Personal About Page": 0.6548414388541375, | |
| "Personal Blog": 0.7900118073388407, | |
| "User Reviews": 0.7621506676545696 | |
| }, | |
| "0.05": { | |
| "Academic Writing": 0.9843446823102748, | |
| "Creative Writing": 0.9699377397101625, | |
| "Knowledge Article": 0.7910862604485897, | |
| "News Article": 0.8612633908883776, | |
| "Nonfiction Writing": 0.743589796841061, | |
| "Personal About Page": 0.9437852253526873, | |
| "Personal Blog": 0.9716472633629923, | |
| "User Reviews": 0.9735735300997738 | |
| } | |
| }, | |
| "per_topic": { | |
| "0.005": { | |
| "Health": 0.02665696794661508 | |
| }, | |
| "0.01": { | |
| "Art & Design": 0.19179490825513668, | |
| "Crime & Law": 0.1545145294205277, | |
| "Education & Jobs": 0.10005740179860542, | |
| "Entertainment": 0.10123568468788725, | |
| "Fashion & Beauty": 0.27768413068085457, | |
| "Finance & Business": 0.050734614106130005, | |
| "Food & Dining": 0.13380622041490592, | |
| "Games": 0.08438404653501116, | |
| "Health": 0.06339366239605145, | |
| "History": 0.48362816444056334, | |
| "Home & Hobbies": 0.15587984819752723, | |
| "Literature": 0.3373619592568417, | |
| "Politics": 0.14492108917514784, | |
| "Religion": 0.08966523650277478, | |
| "Science & Tech.": 0.07745924258865121, | |
| "Social Life": 0.13181006668386752, | |
| "Sports & Fitness": 0.1920630245014839, | |
| "Transportation": 0.12046734651780304, | |
| "Travel": 0.11693166984556584 | |
| }, | |
| "0.02": { | |
| "Art & Design": 0.6284591030746229, | |
| "Crime & Law": 0.6611839703198765, | |
| "Education & Jobs": 0.45501643442833967, | |
| "Entertainment": 0.4548890511046284, | |
| "Fashion & Beauty": 0.6978008965369162, | |
| "Finance & Business": 0.21864830862043488, | |
| "Food & Dining": 0.6699928992936147, | |
| "Games": 0.38033045393989123, | |
| "Hardware": 0.6323141822811568, | |
| "Health": 0.2776615271356264, | |
| "History": 0.734295973796192, | |
| "Home & Hobbies": 0.6196655919922738, | |
| "Industrial": 0.3703898218041191, | |
| "Literature": 0.7246250279042247, | |
| "Politics": 0.6361724103395298, | |
| "Religion": 0.5985515346284275, | |
| "Science & Tech.": 0.38602130217151465, | |
| "Social Life": 0.5687486373093052, | |
| "Software": 0.40516721829317437, | |
| "Software Dev.": 0.45919684576920805, | |
| "Sports & Fitness": 0.6547269797506594, | |
| "Transportation": 0.6111439857345478, | |
| "Travel": 0.6217189547991822 | |
| }, | |
| "0.05": { | |
| "Art & Design": 0.9549211242006862, | |
| "Crime & Law": 0.9683806173907001, | |
| "Education & Jobs": 0.9259304789693195, | |
| "Entertainment": 0.9394194723380223, | |
| "Fashion & Beauty": 0.9615725468619022, | |
| "Finance & Business": 0.7231349175669055, | |
| "Food & Dining": 0.9672438690621314, | |
| "Games": 0.9211352733763646, | |
| "Hardware": 0.9562895651759757, | |
| "Health": 0.8940531615491903, | |
| "History": 0.9718710969119697, | |
| "Home & Hobbies": 0.9552403326066375, | |
| "Industrial": 0.9413230867039313, | |
| "Literature": 0.9672114632363698, | |
| "Politics": 0.963575067014845, | |
| "Religion": 0.9661166095070377, | |
| "Science & Tech.": 0.9546518678503014, | |
| "Social Life": 0.933623815852447, | |
| "Software": 0.9180651875574712, | |
| "Software Dev.": 0.9347538496353965, | |
| "Sports & Fitness": 0.9513339148722936, | |
| "Transportation": 0.9554580679177035, | |
| "Travel": 0.9530263127959078 | |
| } | |
| } | |
| } | |