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

Libraries

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
```

rishanthrajendhran commited on Aug 31

Commit

549f841

verified ·

1 Parent(s): c1ec400

model card

Browse files

Files changed (1) hide show

README.md +15 -28

README.md CHANGED Viewed

@@ -1,37 +1,24 @@
 ---
 base_model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
 library_name: peft
-tags:
-- tinker
-- peft
-- lora
-- base_model:adapter:nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
-tinker_path: tinker://9f500a21-df39-5716-89e5-e65f9390fb14:train:0/sampler_weights/nemo_full_v1m_r64_ep1_final
 ---
-# Tinker LoRA Adapter
-This repository contains a LoRA adapter exported from Tinker.
-## Usage
-```python
-from transformers import AutoModelForCausalLM
-adapter_id = "rishanthrajendhran/ideadet-nemotron30b-1m-outline"
-base_model = "nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16"
-model = AutoModelForCausalLM.from_pretrained(adapter_id, device_map="auto")
-```
-## Source
-```
-tinker://9f500a21-df39-5716-89e5-e65f9390fb14:train:0/sampler_weights/nemo_full_v1m_r64_ep1_final
-```
-## Details
-- Base model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
-- LoRA rank: 64
-- Trained modules: attn=True, mlp=True, unembed=True

 ---
+license: other
+license_name: openmdw-1.1
 base_model: nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16
 library_name: peft
+tags: [ai-detection, lora]
+extra_gated_prompt: "Access is granted individually. Please say who you are and what you intend to use the weights for."
 ---
+# ideadet-nemotron30b-1m-outline
+Idea-level AI-text detector: a LoRA adapter over
+`nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16`. It answers **whose ideas a document
+contains**, not who typed the sentences.
+- **Training corpus:** 1m
+- **Reads at inference:** a de-leaked (paraphrased) role-labelled outline of the document
+Scored by comparing the next-token log-probabilities of the tokens `human` and `ai`, two-way
+normalised, giving P(human). The detector fires when that falls below a calibrated threshold.
+Thresholds are quantiles over held-out human documents and are **not** included here: a cut
+from one model or input form is meaningless against another's scores.
+Fuller documentation and evaluation results to follow.