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 5 days ago

Commit

a8b3857

verified ·

1 Parent(s): 9daf2d1

Card: link the idealens package and GitHub repo

Browse files

Files changed (1) hide show

README.md +13 -2

README.md CHANGED Viewed

@@ -43,10 +43,21 @@ From the paper:
 Scoring a document takes two steps:
 1. **Extract an outline.** An LLM writes the outline from the document, its format's role vocabulary and six worked
-   examples (the paper uses Gemini 3.7 Flash). The prompts and role vocabularies are in the code repository: *link
-   added on publication*. Score the outline as extracted; the paraphrasing step is only for training data.
 2. **Score the outline** with this model, as below.
 ### Load the merged model (66 GB download)
 ```python

 Scoring a document takes two steps:
 1. **Extract an outline.** An LLM writes the outline from the document, its format's role vocabulary and six worked
+   examples (the paper uses Gemini 3.7 Flash). The [idealens](https://github.com/RishanthRajendhran/IdeaLens) package ships the prompts, role vocabularies and
+   worked examples, and runs this step. Score the outline as extracted; the paraphrasing step is only for training data.
 2. **Score the outline** with this model, as below.
+### With the idealens package
+[idealens](https://github.com/RishanthRajendhran/IdeaLens) ([PyPI](https://pypi.org/project/idealens/)) runs every step, scores with this model on vLLM and applies the thresholds in this repo:
+```bash
+pip install "idealens[vllm]"
+idealens run docs.jsonl -o scores.jsonl --model IdeaLens
+```
+Input is JSONL with a `text` field per document. The rest of this section runs the model directly.
 ### Load the merged model (66 GB download)
 ```python