Instructions to use leonvanbokhorst/deepseek-r1-uncertainty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonvanbokhorst/deepseek-r1-uncertainty with Transformers:

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

pipe = pipeline("text-generation", model="leonvanbokhorst/deepseek-r1-uncertainty")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("leonvanbokhorst/deepseek-r1-uncertainty", dtype="auto")

llama-cpp-python

How to use leonvanbokhorst/deepseek-r1-uncertainty with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="leonvanbokhorst/deepseek-r1-uncertainty",
	filename="deepseek-r1-uncertainty-q5_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use leonvanbokhorst/deepseek-r1-uncertainty 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 leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

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 leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

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 leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

Use Docker

docker model run hf.co/leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

LM Studio
Jan

vLLM

How to use leonvanbokhorst/deepseek-r1-uncertainty with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leonvanbokhorst/deepseek-r1-uncertainty"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "leonvanbokhorst/deepseek-r1-uncertainty",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

SGLang

How to use leonvanbokhorst/deepseek-r1-uncertainty 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 "leonvanbokhorst/deepseek-r1-uncertainty" \
    --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": "leonvanbokhorst/deepseek-r1-uncertainty",
		"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 "leonvanbokhorst/deepseek-r1-uncertainty" \
        --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": "leonvanbokhorst/deepseek-r1-uncertainty",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use leonvanbokhorst/deepseek-r1-uncertainty with Ollama:
```
ollama run hf.co/leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
```

Unsloth Studio

How to use leonvanbokhorst/deepseek-r1-uncertainty 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 leonvanbokhorst/deepseek-r1-uncertainty 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 leonvanbokhorst/deepseek-r1-uncertainty to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for leonvanbokhorst/deepseek-r1-uncertainty to start chatting

Atomic Chat new
Docker Model Runner
How to use leonvanbokhorst/deepseek-r1-uncertainty with Docker Model Runner:
```
docker model run hf.co/leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M
```

Lemonade

How to use leonvanbokhorst/deepseek-r1-uncertainty with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull leonvanbokhorst/deepseek-r1-uncertainty:Q5_K_M

Run and chat with the model

lemonade run user.deepseek-r1-uncertainty-Q5_K_M

List all available models

lemonade list

deepseek-r1-uncertainty

File size: 1,744 Bytes

da1955b

---
base_model: unsloth/DeepSeek-R1-Distill-Qwen-7B-unsloth-bnb-4bit
library_name: transformers
license: apache-2.0
datasets:
- leonvanbokhorst/friction-uncertainty-v2
language:
- en
tags:
- ai-safety
- ai-friction
- human-like-messiness
- ai-uncertainty
pipeline_tag: text-generation
---

# Friction Reasoning Model

This model is fine-tuned to respond in an uncertain manner. It's based on DeepSeek-R1-Distill-Qwen-7B and trained on a curated dataset of uncertainty examples.

## Model Description

- **Model Architecture**: DeepSeek-R1-Distill-Qwen-7B with LoRA adapters
- **Language(s)**: English
- **License**: Apache 2.0
- **Finetuning Approach**: Instruction tuning with friction-based reasoning examples


### Limitations

The model:
- Is not designed for factual question-answering
- May sometimes be overly uncertain
- Should not be used for medical, legal, or financial advice
- May not perform well on objective or factual tasks

### Bias and Risks

The model:
- May exhibit biases present in the training data
- Could potentially reinforce uncertainty in certain situations
- Might challenge user assumptions in sensitive contexts
- Should be used with appropriate content warnings




## Citation

If you use this model in your research, please cite:

```bibtex
@misc{friction-reasoning-2025,
  author = {Leon van Bokhorst},
  title = {Mixture of Friction: Fine-tuned Language Model for Uncertainty},
  year = {2025},
  publisher = {HuggingFace},
  journal = {HuggingFace Model Hub},
  howpublished = {\url{https://huggingface.co/leonvanbokhorst/deepseek-r1-uncertainty}}
}
```

## Acknowledgments

- DeepSeek AI for the base model
- Unsloth team for the optimization toolkit
- HuggingFace for the model hosting and infrastructure