Instructions to use sebsigma/SemanticCite-Checker-Qwen3-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Transformers:

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

pipe = pipeline("text-generation", model="sebsigma/SemanticCite-Checker-Qwen3-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sebsigma/SemanticCite-Checker-Qwen3-4B", dtype="auto")

llama-cpp-python

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="sebsigma/SemanticCite-Checker-Qwen3-4B",
	filename="unsloth.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
# Run inference directly in the terminal:
llama-cli -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
# Run inference directly in the terminal:
llama-cli -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
# Run inference directly in the terminal:
./llama-cli -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Use Docker

docker model run hf.co/sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

LM Studio
Jan

vLLM

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sebsigma/SemanticCite-Checker-Qwen3-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sebsigma/SemanticCite-Checker-Qwen3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

SGLang

How to use sebsigma/SemanticCite-Checker-Qwen3-4B 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 "sebsigma/SemanticCite-Checker-Qwen3-4B" \
    --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": "sebsigma/SemanticCite-Checker-Qwen3-4B",
		"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 "sebsigma/SemanticCite-Checker-Qwen3-4B" \
        --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": "sebsigma/SemanticCite-Checker-Qwen3-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Ollama:
```
ollama run hf.co/sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
```

Unsloth Studio new

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sebsigma/SemanticCite-Checker-Qwen3-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for sebsigma/SemanticCite-Checker-Qwen3-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for sebsigma/SemanticCite-Checker-Qwen3-4B to start chatting

Pi new

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sebsigma/SemanticCite-Checker-Qwen3-4B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Run Hermes

hermes

Docker Model Runner
How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Docker Model Runner:
```
docker model run hf.co/sebsigma/SemanticCite-Checker-Qwen3-4B:BF16
```

Lemonade

How to use sebsigma/SemanticCite-Checker-Qwen3-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sebsigma/SemanticCite-Checker-Qwen3-4B:BF16

Run and chat with the model

lemonade run user.SemanticCite-Checker-Qwen3-4B-BF16

List all available models

lemonade list

SemanticCite-Checker-Qwen3-4B

A fine-tuned Qwen3-4B model specialized for citation verification and reference checking. This model analyses citation claims against reference text snippets and provides structured JSON output with classification, reasoning, and confidence scores.

Model Details

Model Description

This model is designed to verify the accuracy of citation claims by comparing them against reference text snippets. It classifies citations into four categories (SUPPORTED, PARTIALLY_SUPPORTED, UNSUPPORTED, UNCERTAIN) and provides detailed reasoning with confidence scores in structured JSON format.

Developed by: Sebastian Haan
Model type: Causal Language Model (Fine-tuned)
Language(s) (NLP): English
License: MIT
Finetuned from model: unsloth/Qwen3-4B-unsloth-bnb-4bit

Uses

Direct Use

This model is intended for:

Academic semantic citation verification
Citation contectual understanding
Automated reference validation in scholarly writing

Out-of-Scope Use

This model should not be used for:

General text classification tasks outside of citation verification
Legal document verification (requires domain-specific expertise)
Medical or health-related claim verification
Decision-making in high-stakes scenarios without human oversight

Downloads last month: 32

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

4-bit

16-bit

Model tree for sebsigma/SemanticCite-Checker-Qwen3-4B

Base model

Qwen/Qwen3-4B-Base

Finetuned

Qwen/Qwen3-4B

Quantized

unsloth/Qwen3-4B-unsloth-bnb-4bit

Adapter

(27)

this model

sebsigma
/

SemanticCite-Checker-Qwen3-4B