Text Generation
Transformers
Safetensors
English
nemotron_h
ai-text-detection
idea-provenance
conversational
Instructions to use rishanthrajendhran/IdeaLens with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rishanthrajendhran/IdeaLens with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rishanthrajendhran/IdeaLens") 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") model = AutoModelForCausalLM.from_pretrained("rishanthrajendhran/IdeaLens", 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 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rishanthrajendhran/IdeaLens" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/rishanthrajendhran/IdeaLens
- SGLang
How to use rishanthrajendhran/IdeaLens 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use rishanthrajendhran/IdeaLens with Docker Model Runner:
docker model run hf.co/rishanthrajendhran/IdeaLens
Download thresholds.json from rishanthrajendhran/IdeaLens: direct link, hf CLI and curl.
- Browser
- Download file 8.42 kB
-
https://huggingface.co/rishanthrajendhran/IdeaLens/resolve/main/thresholds.json
- Command line
-
hf download hf://rishanthrajendhran/IdeaLens/thresholds.json
-
curl -L -o thresholds.json https://huggingface.co/rishanthrajendhran/IdeaLens/resolve/main/thresholds.json
8.42 kB
| { | |
| "model": "rishanthrajendhran/IdeaLens", | |
| "input": "outline", | |
| "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 | |
| }, | |
| "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, | |
| 0.1, | |
| 0.2 | |
| ], | |
| "global": { | |
| "0.001": 0.016910312716982526, | |
| "0.005": 0.0727912234663859, | |
| "0.01": 0.13711697951409052, | |
| "0.02": 0.34047000148262974, | |
| "0.05": 0.7271281470504786, | |
| "0.1": 0.8979844557423863, | |
| "0.2": 0.9715639649482037 | |
| }, | |
| "per_format": { | |
| "0.005": { | |
| "Academic Writing": 0.16045022213923377, | |
| "Creative Writing": 0.20831380041381886, | |
| "Knowledge Article": 0.06944307847622781, | |
| "News Article": 0.04970034027733887, | |
| "Nonfiction Writing": 0.05358019572554795, | |
| "Personal About Page": 0.08279563922829149, | |
| "Personal Blog": 0.09459867455364941, | |
| "User Reviews": 0.08956495554856032 | |
| }, | |
| "0.01": { | |
| "Academic Writing": 0.37109147193653746, | |
| "Creative Writing": 0.35315202590355255, | |
| "Knowledge Article": 0.10699845694815548, | |
| "News Article": 0.09169947897393349, | |
| "Nonfiction Writing": 0.08988523605364404, | |
| "Personal About Page": 0.17823972354705253, | |
| "Personal Blog": 0.252138067958794, | |
| "User Reviews": 0.2474776779563594 | |
| }, | |
| "0.02": { | |
| "Academic Writing": 0.6308181452463197, | |
| "Creative Writing": 0.5494257367208046, | |
| "Knowledge Article": 0.20818577828129917, | |
| "News Article": 0.23609769559580096, | |
| "Nonfiction Writing": 0.1702161018814425, | |
| "Personal About Page": 0.39822976725655357, | |
| "Personal Blog": 0.5059899682776587, | |
| "User Reviews": 0.46654259421577626 | |
| }, | |
| "0.05": { | |
| "Academic Writing": 0.8728532107534116, | |
| "Creative Writing": 0.7902505745923557, | |
| "Knowledge Article": 0.6116968523774344, | |
| "News Article": 0.5751623415922733, | |
| "Nonfiction Writing": 0.5406010407175179, | |
| "Personal About Page": 0.7151932503758738, | |
| "Personal Blog": 0.7859592106635767, | |
| "User Reviews": 0.7977432441876905 | |
| }, | |
| "0.1": { | |
| "Academic Writing": 0.9530998558087874, | |
| "Creative Writing": 0.9077686152037142, | |
| "Knowledge Article": 0.8856753919316568, | |
| "News Article": 0.8128760568899319, | |
| "Nonfiction Writing": 0.8672406525378675, | |
| "Personal About Page": 0.875891090140992, | |
| "Personal Blog": 0.9098900057291042, | |
| "User Reviews": 0.9305423350548957 | |
| }, | |
| "0.2": { | |
| "Academic Writing": 0.986172454554451, | |
| "Creative Writing": 0.9693178579613291, | |
| "Knowledge Article": 0.9718952211059724, | |
| "News Article": 0.9488666358861433, | |
| "Nonfiction Writing": 0.9693953589738518, | |
| "Personal About Page": 0.9596987773216274, | |
| "Personal Blog": 0.9715717592434062, | |
| "User Reviews": 0.979949437506 | |
| } | |
| }, | |
| "per_topic": { | |
| "0.005": { | |
| "Health": 0.0610965620278734 | |
| }, | |
| "0.01": { | |
| "Art & Design": 0.15667222645558712, | |
| "Crime & Law": 0.17453283749851123, | |
| "Education & Jobs": 0.12886971723833102, | |
| "Entertainment": 0.11819848263314488, | |
| "Fashion & Beauty": 0.19402951675056096, | |
| "Finance & Business": 0.08572843930921965, | |
| "Food & Dining": 0.14630909239840253, | |
| "Games": 0.15453482900524618, | |
| "Health": 0.10373515806574425, | |
| "History": 0.29504311061590965, | |
| "Home & Hobbies": 0.15140165883881962, | |
| "Literature": 0.26503532923819956, | |
| "Politics": 0.1619589695853355, | |
| "Religion": 0.14841329925315658, | |
| "Science & Tech.": 0.12006036270304885, | |
| "Social Life": 0.1442337581078069, | |
| "Sports & Fitness": 0.1556513531897659, | |
| "Transportation": 0.16223792819723193, | |
| "Travel": 0.1519828129273214 | |
| }, | |
| "0.02": { | |
| "Art & Design": 0.4280061668013947, | |
| "Crime & Law": 0.40561762457043543, | |
| "Education & Jobs": 0.29982288352803077, | |
| "Entertainment": 0.3163174178232409, | |
| "Fashion & Beauty": 0.4397407287216919, | |
| "Finance & Business": 0.19542480230231465, | |
| "Food & Dining": 0.41991528476107876, | |
| "Games": 0.31968682164703643, | |
| "Hardware": 0.4006567652124695, | |
| "Health": 0.23449671053584442, | |
| "History": 0.5251195670244517, | |
| "Home & Hobbies": 0.38437374945110886, | |
| "Industrial": 0.3225212937566969, | |
| "Literature": 0.4849538444497918, | |
| "Politics": 0.38385972823963466, | |
| "Religion": 0.372733898808676, | |
| "Science & Tech.": 0.2887184864382761, | |
| "Social Life": 0.3682838321103103, | |
| "Software": 0.3155424928082715, | |
| "Software Dev.": 0.34228390137318565, | |
| "Sports & Fitness": 0.3830791104739714, | |
| "Transportation": 0.36325003721271887, | |
| "Travel": 0.3885504044253141 | |
| }, | |
| "0.05": { | |
| "Art & Design": 0.7597071882301957, | |
| "Crime & Law": 0.780120173112018, | |
| "Education & Jobs": 0.6843937052521694, | |
| "Entertainment": 0.7023000639381533, | |
| "Fashion & Beauty": 0.7611377016113461, | |
| "Finance & Business": 0.5400477693581884, | |
| "Food & Dining": 0.763537797714218, | |
| "Games": 0.6972255925673959, | |
| "Hardware": 0.7512266971694413, | |
| "Health": 0.6404475581508516, | |
| "History": 0.8207972392785796, | |
| "Home & Hobbies": 0.7400915904992856, | |
| "Industrial": 0.7206672236836739, | |
| "Literature": 0.8028438257230146, | |
| "Politics": 0.7539958067871309, | |
| "Religion": 0.7538633727349769, | |
| "Science & Tech.": 0.722352087567979, | |
| "Social Life": 0.7070066374914504, | |
| "Software": 0.6914290614985231, | |
| "Software Dev.": 0.7119256116638377, | |
| "Sports & Fitness": 0.7377047523780582, | |
| "Transportation": 0.7572048153947143, | |
| "Travel": 0.7482741143847389 | |
| }, | |
| "0.1": { | |
| "Art & Design": 0.8981931472325196, | |
| "Crime & Law": 0.9261916076649587, | |
| "Education & Jobs": 0.8790306884482433, | |
| "Entertainment": 0.8847521012776755, | |
| "Fashion & Beauty": 0.9072102421299174, | |
| "Finance & Business": 0.8300942006962467, | |
| "Food & Dining": 0.9160360598227963, | |
| "Games": 0.8839461258280736, | |
| "Hardware": 0.9135252867173463, | |
| "Health": 0.8760115221437729, | |
| "History": 0.9342834605519144, | |
| "Home & Hobbies": 0.9061145176620977, | |
| "Industrial": 0.9121142941512812, | |
| "Literature": 0.9270873095006306, | |
| "Politics": 0.90569635176809, | |
| "Religion": 0.9041726973466222, | |
| "Science & Tech.": 0.9006011074265093, | |
| "Social Life": 0.8744661330901681, | |
| "Software": 0.8868625173737146, | |
| "Software Dev.": 0.8864542661539737, | |
| "Sports & Fitness": 0.896828946457183, | |
| "Transportation": 0.9072735689447999, | |
| "Travel": 0.903022411616813 | |
| }, | |
| "0.2": { | |
| "Art & Design": 0.9717865748974299, | |
| "Crime & Law": 0.9804893342045466, | |
| "Education & Jobs": 0.9653651444404299, | |
| "Entertainment": 0.9669234232261763, | |
| "Fashion & Beauty": 0.9733219483232493, | |
| "Finance & Business": 0.9541064927185746, | |
| "Food & Dining": 0.974576902291307, | |
| "Games": 0.9643208598145052, | |
| "Hardware": 0.976339570614405, | |
| "Health": 0.9703820510794805, | |
| "History": 0.9815161284086212, | |
| "Home & Hobbies": 0.9738259921918783, | |
| "Industrial": 0.977210381175326, | |
| "Literature": 0.9783407624293717, | |
| "Politics": 0.971848666067582, | |
| "Religion": 0.9725396125616405, | |
| "Science & Tech.": 0.9757920150474068, | |
| "Social Life": 0.9549998512650022, | |
| "Software": 0.9713883872658887, | |
| "Software Dev.": 0.9668914651292468, | |
| "Sports & Fitness": 0.9712907798714918, | |
| "Transportation": 0.9746075132160322, | |
| "Travel": 0.9706252625914265 | |
| } | |
| } | |
| } | |