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
```

rishanthrajendhran commited on Oct 1

Commit

b5d8bf3

verified ·

1 Parent(s): 81f842b

Card: quick start with the idealens package (classify, extract, score)

Browse files

Files changed (1) hide show

README.md +16 -3

README.md CHANGED Viewed

@@ -33,16 +33,29 @@ identically on outlines, reaches 95.3% and 81.3%; Pangram 4 reaches 98.5% and 25
 Pass the document text as is.
-### With the idealens package
-[idealens](https://github.com/RishanthRajendhran/IdeaLens) ([PyPI](https://pypi.org/project/idealens/)) scores with this model on vLLM and applies the thresholds in this repo:
 ```bash
 pip install "idealens[vllm]"
 idealens score docs.jsonl -o scores.jsonl --model ProseLens
 ```
-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)

 Pass the document text as is.
+### Quick start with the idealens package
+[idealens](https://github.com/RishanthRajendhran/IdeaLens) ([PyPI](https://pypi.org/project/idealens/)) scores documents with ProseLens on vLLM and applies the thresholds in this repo. No
+outline and no LLM call is needed:
 ```bash
 pip install "idealens[vllm]"
 idealens score docs.jsonl -o scores.jsonl --model ProseLens
 ```
+Input is JSONL with a `text` field per document. In Python:
+```python
+import idealens as il
+with il.Detector("ProseLens") as det:         # vLLM, with this repo's thresholds
+    records = det.score_documents([open("document.txt").read()])
+print(records[0]["p_human"], records[0]["verdict"]["ai"])
+```
+To compare with idea-level detection on the same documents, `idealens run docs.jsonl -o ideas.jsonl --model IdeaLens`
+extracts their outlines and scores them with [IdeaLens](https://huggingface.co/rishanthrajendhran/IdeaLens). The rest
+of this section runs the model directly.
 ### Load the merged model (66 GB download)