Instructions to use afrideva/refusal-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use afrideva/refusal-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="afrideva/refusal-GGUF") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("afrideva/refusal-GGUF", dtype="auto") - llama-cpp-python
How to use afrideva/refusal-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="afrideva/refusal-GGUF", filename="refusal.Q2_K.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 afrideva/refusal-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/refusal-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/refusal-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf afrideva/refusal-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf afrideva/refusal-GGUF:Q4_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 afrideva/refusal-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf afrideva/refusal-GGUF:Q4_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 afrideva/refusal-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf afrideva/refusal-GGUF:Q4_K_M
Use Docker
docker model run hf.co/afrideva/refusal-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use afrideva/refusal-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "afrideva/refusal-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "afrideva/refusal-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/afrideva/refusal-GGUF:Q4_K_M
- SGLang
How to use afrideva/refusal-GGUF 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 "afrideva/refusal-GGUF" \ --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": "afrideva/refusal-GGUF", "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 "afrideva/refusal-GGUF" \ --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": "afrideva/refusal-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use afrideva/refusal-GGUF with Ollama:
ollama run hf.co/afrideva/refusal-GGUF:Q4_K_M
- Unsloth Studio new
How to use afrideva/refusal-GGUF 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 afrideva/refusal-GGUF 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 afrideva/refusal-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for afrideva/refusal-GGUF to start chatting
- Docker Model Runner
How to use afrideva/refusal-GGUF with Docker Model Runner:
docker model run hf.co/afrideva/refusal-GGUF:Q4_K_M
- Lemonade
How to use afrideva/refusal-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull afrideva/refusal-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.refusal-GGUF-Q4_K_M
List all available models
lemonade list
refusal-GGUF
Quantized GGUF model files for refusal from mrfakename
Original Model Card:
I messed up on the previous model. This is a fixed version.
A tiny 1B model that refuses basically anything you ask it! Trained on the refusal dataset. Prompt format is ChatML.
Training results:
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.4352 | 0.0580 | 1 | 2.4462 |
| 1.5741 | 0.5217 | 9 | 1.4304 |
| 1.5204 | 1.0435 | 18 | 1.3701 |
| 1.0794 | 1.5217 | 27 | 1.3505 |
| 1.1275 | 2.0435 | 36 | 1.3344 |
| 0.6652 | 2.5217 | 45 | 1.4360 |
| 0.6248 | 3.0435 | 54 | 1.4313 |
| 0.6142 | 3.5072 | 63 | 1.4934 |
Training hyperparemeters:
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Base model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
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