Text Generation
GGUF
Rust
English
ruvllm
agent-routing
claude-code
recursive-language-model
embeddings
llm-inference
sona
hnsw
simd
imatrix
conversational
Instructions to use Princess3/Hpp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Princess3/Hpp 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 Princess3/Hpp:Q4_K_M # Run inference directly in the terminal: llama cli -hf Princess3/Hpp:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Princess3/Hpp:Q4_K_M # Run inference directly in the terminal: llama cli -hf Princess3/Hpp: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 Princess3/Hpp:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Princess3/Hpp: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 Princess3/Hpp:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Princess3/Hpp:Q4_K_M
Use Docker
docker model run hf.co/Princess3/Hpp:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Princess3/Hpp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Princess3/Hpp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Princess3/Hpp", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Princess3/Hpp:Q4_K_M
- Ollama
How to use Princess3/Hpp with Ollama:
ollama run hf.co/Princess3/Hpp:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use Princess3/Hpp with Docker Model Runner:
docker model run hf.co/Princess3/Hpp:Q4_K_M
- Lemonade
How to use Princess3/Hpp with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Princess3/Hpp:Q4_K_M
Run and chat with the model
lemonade run user.Hpp-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,041 Bytes
51c3ba6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 | {
"version": "2.4",
"release_date": "2026-01-20",
"sota_metrics": {
"total_triplets": 2545,
"base_triplets": 1078,
"ecosystem_triplets": 1467,
"embedding_accuracy": 0.8823,
"hard_negative_accuracy": 0.8117,
"hybrid_routing_accuracy": 1.0,
"validation_tests": 62,
"validation_accuracy": 1.0
},
"capabilities": {
"claude_flow": {
"cli_commands": 26,
"subcommands": 179,
"agent_types": 58,
"hooks": 27,
"workers": 12,
"skills": 29
},
"agentic_flow": {
"capabilities": 18,
"cli_commands": 17,
"agent_types": 33,
"mcp_tools": 32,
"learning_algorithms": 9
},
"ruvector": {
"rust_crates": 22,
"npm_packages": 12,
"cli_commands": 6,
"attention_types": 6,
"graph_algorithms": 4,
"hardware_backends": 3
}
},
"training_config": {
"epochs": 30,
"batch_size": 32,
"learning_rate": 2e-05,
"loss": "triplet + infonce",
"margin": 0.5,
"temperature": 0.07
}
}
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