Instructions to use sebsigma/SemanticCite-Refiner-Qwen3-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Transformers:

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

pipe = pipeline("text-generation", model="sebsigma/SemanticCite-Refiner-Qwen3-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sebsigma/SemanticCite-Refiner-Qwen3-1B")
model = AutoModelForCausalLM.from_pretrained("sebsigma/SemanticCite-Refiner-Qwen3-1B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16
# Run inference directly in the terminal:
llama cli -hf sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16
# Run inference directly in the terminal:
llama cli -hf sebsigma/SemanticCite-Refiner-Qwen3-1B: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-Refiner-Qwen3-1B:BF16
# Run inference directly in the terminal:
./llama-cli -hf sebsigma/SemanticCite-Refiner-Qwen3-1B: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-Refiner-Qwen3-1B:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

Use Docker

docker model run hf.co/sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

LM Studio
Jan

vLLM

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sebsigma/SemanticCite-Refiner-Qwen3-1B"
# 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-Refiner-Qwen3-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

SGLang

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B 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-Refiner-Qwen3-1B" \
    --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-Refiner-Qwen3-1B",
		"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-Refiner-Qwen3-1B" \
        --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-Refiner-Qwen3-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Ollama:
```
ollama run hf.co/sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16
```

Unsloth Studio

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B 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-Refiner-Qwen3-1B 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-Refiner-Qwen3-1B 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-Refiner-Qwen3-1B to start chatting

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sebsigma/SemanticCite-Refiner-Qwen3-1B: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-Refiner-Qwen3-1B:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Docker Model Runner:
```
docker model run hf.co/sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16
```

Lemonade

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sebsigma/SemanticCite-Refiner-Qwen3-1B:BF16

Run and chat with the model

lemonade run user.SemanticCite-Refiner-Qwen3-1B-BF16

List all available models

lemonade list

Hermes Agent

How to use sebsigma/SemanticCite-Refiner-Qwen3-1B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sebsigma/SemanticCite-Refiner-Qwen3-1B: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-Refiner-Qwen3-1B:BF16

Run Hermes

hermes

Atomic Chat

SemanticCite-Refiner-Qwen3-1B

A fine-tuned Qwen3-1.7B model specialized for preprocessing citation text. This model removes reference markers, author names, and publication identifiers while converting author-centered statements to fact-centered statements for improved citation verification.

Model Details

Model Description

This model is designed to preprocess citation text by cleaning and standardizing it for downstream verification tasks. It removes reference markers (e.g., [1], Smith 2020, et al.), converts author-centered statements to fact-centered statements using passive voice, while maintaining all numerical values and factual details.

Developed by: Sebastian Haan
Model type: Causal Language Model (Fine-tuned)
Language(s) (NLP): English
License: MIT
Finetuned from model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit

Uses

Direct Use

This model is intended for:

Preprocessing citation text for academic verification systems
Cleaning and standardizing citation statements
Converting author-centric to fact-centric statements
First stage in citation verification pipelines

Out-of-Scope Use

This model should not be used for:

General text summarization or rewriting
Legal document processing
Medical text processing
Creative writing or content generation

Downloads last month: 49

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for sebsigma/SemanticCite-Refiner-Qwen3-1B

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

unsloth/Qwen3-1.7B-unsloth-bnb-4bit

Adapter

(18)

this model

sebsigma
/

SemanticCite-Refiner-Qwen3-1B

SemanticCite-Refiner-Qwen3-1B

Model Details

Model Description

Uses

Direct Use

Out-of-Scope Use

Model tree for sebsigma/SemanticCite-Refiner-Qwen3-1B

Space using sebsigma/SemanticCite-Refiner-Qwen3-1B 1