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 Desktop

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 @earendil-works/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

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

OpenClaw

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"

SemanticCite-Refiner-Qwen3-1B

File size: 1,780 Bytes

---
base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
license: mit
pipeline_tag: text-generation
tags:
- base_model:adapter:unsloth/Qwen3-1.7B-unsloth-bnb-4bit
- lora
- sft
- transformers
- trl
- unsloth
- citation-preprocessing
- text-refinement
- academic-text-processing
inference:
  parameters:
    temperature: 0.6
    top_p: 0.95
    max_new_tokens: 512
ollama:
  parameters:
    num_ctx: 1024
    temperature: 0.6
    top_p: 0.95
---

# 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