Instructions to use comp4cls/comp4cls-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use comp4cls/comp4cls-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="comp4cls/comp4cls-4B")
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("comp4cls/comp4cls-4B")
model = AutoModelForCausalLM.from_pretrained("comp4cls/comp4cls-4B", 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 comp4cls/comp4cls-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "comp4cls/comp4cls-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "comp4cls/comp4cls-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/comp4cls/comp4cls-4B

SGLang

How to use comp4cls/comp4cls-4B 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 "comp4cls/comp4cls-4B" \
    --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": "comp4cls/comp4cls-4B",
		"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 "comp4cls/comp4cls-4B" \
        --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": "comp4cls/comp4cls-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use comp4cls/comp4cls-4B with Docker Model Runner:
```
docker model run hf.co/comp4cls/comp4cls-4B
```

chanuk commited on Oct 31, 2025

Commit

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@@ -6,11 +6,13 @@ tags: []
 # Model Description
 **Comp4Cls** is a retrieval-augmented classification framework that uses **entity-centric semantic compression** to turn long scientific/technical documents into short, task-focused representations for both retrieval and labeling. Documents (papers, patents, and R&D reports) are first compressed into structured summaries that preserve discriminative signals (e.g., core concepts, methods, problems, findings), embedded, and stored in a vector DB. At inference, a query is compressed the same way, nearest neighbors are retrieved, and a small LLM assigns the final class label using the compressed evidence.
 The end-to-end workflow—**Phase 1: compression + indexing, Phase 2: retrieval + classification**—is illustrated in the framework diagram on *page 2*. Experiments on a large bilingual corpus with hierarchical, multi-label taxonomies show that a **4B-scale** Comp4Cls matches or outperforms **8B–14B** models, especially in fine-grained categories, while cutting token usage and compute. Moderate compression (often **~20% of entities**) preserves retrieval fidelity and boosts downstream F1, enabling lightweight, low-latency deployment in production pipelines. See *Table II on page 8* (compression vs. length), *Figure 6 on page 9* (retrieval quality under compression), and *Figure 7 on page 10* (accuracy vs. larger LLMs).
 ## Framework Diagram
-![Comp4Cls Framework](comp4cls.pdf)
 ## Model Details

 # Model Description
 **Comp4Cls** is a retrieval-augmented classification framework that uses **entity-centric semantic compression** to turn long scientific/technical documents into short, task-focused representations for both retrieval and labeling. Documents (papers, patents, and R&D reports) are first compressed into structured summaries that preserve discriminative signals (e.g., core concepts, methods, problems, findings), embedded, and stored in a vector DB. At inference, a query is compressed the same way, nearest neighbors are retrieved, and a small LLM assigns the final class label using the compressed evidence.
 The end-to-end workflow—**Phase 1: compression + indexing, Phase 2: retrieval + classification**—is illustrated in the framework diagram on *page 2*. Experiments on a large bilingual corpus with hierarchical, multi-label taxonomies show that a **4B-scale** Comp4Cls matches or outperforms **8B–14B** models, especially in fine-grained categories, while cutting token usage and compute. Moderate compression (often **~20% of entities**) preserves retrieval fidelity and boosts downstream F1, enabling lightweight, low-latency deployment in production pipelines. See *Table II on page 8* (compression vs. length), *Figure 6 on page 9* (retrieval quality under compression), and *Figure 7 on page 10* (accuracy vs. larger LLMs).
 ## Framework Diagram
+![Comp4Cls Framework](Comp4Cls.pdf)
 ## Model Details