Text Generation
Transformers
Safetensors
GGUF
English
mistral
mergekit
Merge
unsloth
Cyber-Series
custom_code
text-generation-inference
Instructions to use LeroyDyer/SpydazWebAI_QuietStar_Project with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeroyDyer/SpydazWebAI_QuietStar_Project with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LeroyDyer/SpydazWebAI_QuietStar_Project", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LeroyDyer/SpydazWebAI_QuietStar_Project", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("LeroyDyer/SpydazWebAI_QuietStar_Project", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use LeroyDyer/SpydazWebAI_QuietStar_Project 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 LeroyDyer/SpydazWebAI_QuietStar_Project:F16 # Run inference directly in the terminal: llama cli -hf LeroyDyer/SpydazWebAI_QuietStar_Project:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf LeroyDyer/SpydazWebAI_QuietStar_Project:F16 # Run inference directly in the terminal: llama cli -hf LeroyDyer/SpydazWebAI_QuietStar_Project:F16
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 LeroyDyer/SpydazWebAI_QuietStar_Project:F16 # Run inference directly in the terminal: ./llama-cli -hf LeroyDyer/SpydazWebAI_QuietStar_Project:F16
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 LeroyDyer/SpydazWebAI_QuietStar_Project:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf LeroyDyer/SpydazWebAI_QuietStar_Project:F16
Use Docker
docker model run hf.co/LeroyDyer/SpydazWebAI_QuietStar_Project:F16
- LM Studio
- Jan
- vLLM
How to use LeroyDyer/SpydazWebAI_QuietStar_Project with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LeroyDyer/SpydazWebAI_QuietStar_Project" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LeroyDyer/SpydazWebAI_QuietStar_Project", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LeroyDyer/SpydazWebAI_QuietStar_Project:F16
- SGLang
How to use LeroyDyer/SpydazWebAI_QuietStar_Project 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 "LeroyDyer/SpydazWebAI_QuietStar_Project" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LeroyDyer/SpydazWebAI_QuietStar_Project", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "LeroyDyer/SpydazWebAI_QuietStar_Project" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LeroyDyer/SpydazWebAI_QuietStar_Project", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use LeroyDyer/SpydazWebAI_QuietStar_Project with Ollama:
ollama run hf.co/LeroyDyer/SpydazWebAI_QuietStar_Project:F16
- Unsloth Desktop
- Docker Model Runner
How to use LeroyDyer/SpydazWebAI_QuietStar_Project with Docker Model Runner:
docker model run hf.co/LeroyDyer/SpydazWebAI_QuietStar_Project:F16
- Lemonade
How to use LeroyDyer/SpydazWebAI_QuietStar_Project with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LeroyDyer/SpydazWebAI_QuietStar_Project:F16
Run and chat with the model
lemonade run user.SpydazWebAI_QuietStar_Project-F16
List all available models
lemonade list
- Atomic Chat
Download mm_projector.bin from LeroyDyer/SpydazWebAI_QuietStar_Project: direct link, hf CLI and curl.
- Browser
- Download file 42 MB
-
https://huggingface.co/LeroyDyer/SpydazWebAI_QuietStar_Project/resolve/main/mm_projector.bin
- Command line
-
hf download hf://LeroyDyer/SpydazWebAI_QuietStar_Project/mm_projector.bin
-
curl -L -o mm_projector.bin https://huggingface.co/LeroyDyer/SpydazWebAI_QuietStar_Project/resolve/main/mm_projector.bin
42 MB
- Xet hash:
- 5c3194c3a440f11b62a520c2205afa59f65c3ea0a86640136508d44d8f328f5c
- Size of remote file:
- 42 MB
- SHA256:
- be33a3b477e091832a3c8a9fabf5769c43b4cb2c161fc8c464b7bb214f16143a
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