DeepSeek-R1-Distill-Qwen-14B

Instructions to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B 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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
# Run inference directly in the terminal:
llama cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
# Run inference directly in the terminal:
llama cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
# Run inference directly in the terminal:
./llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

Use Docker

docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

LM Studio
Jan
Ollama
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with Ollama:
```
ollama run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
```

Unsloth Studio

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B 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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B 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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VECTORVV1/DeepSeek-R1-Distill-Qwen-14B to start chatting

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

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": "VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with Docker Model Runner:
```
docker model run hf.co/VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4
```

Lemonade

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-14B-MXFP4

List all available models

lemonade list

Hermes Agent

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

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 VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use VECTORVV1/DeepSeek-R1-Distill-Qwen-14B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4

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 "VECTORVV1/DeepSeek-R1-Distill-Qwen-14B:MXFP4" \
  --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"

GPT-OSS-20B-Uncensored-HauhauCS-Aggressive

Join the Discord for updates, roadmaps, projects, or just to chat.

Uncensored version of GPT-OSS 20B. This is the aggressive variant - tuned harder for fewer refusals.

If you want something more conservative, check out the Balanced variant.

Format

MXFP4 GGUF. Works with llama.cpp, ollama, LM Studio, and anything else that loads GGUFs.

LM Studio

Compatible with Reasoning Effort custom buttons. To use them, put the model in:

LM Models\lmstudio-community\gpt-oss-20b-GGUF\

About

Base model: GPT-OSS 20B
Abliterated with custom tooling
Pick this one if you want maximum uncensoring

Downloads last month: 19

GGUF

Model size

21B params

Architecture

gpt-oss

Hardware compatibility

4-bit

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