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: 1,839 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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | {
"version": "2.5",
"release_name": "Performance Optimized Edition",
"release_date": "2026-01-21T10:46:53.928251",
"optimizations": {
"hnsw_index": {
"description": "Hierarchical Navigable Small World graphs",
"improvement": "10x faster search at 10k entries"
},
"lru_cache": {
"description": "O(1) LRU cache using Rust lru crate",
"lookup_time_ns": 23.5
},
"zero_copy": {
"description": "Arc<str> string interning",
"improvement": "100-1000x cache improvement"
},
"batch_simd": {
"description": "AVX2/NEON vectorization",
"improvement": "4x throughput"
},
"memory_pools": {
"description": "Arena allocation",
"improvement": "50% fewer allocations"
}
},
"benchmarks": {
"query_decomposition_ns": 340,
"cache_lookup_ns": 23.5,
"memory_search_10k_ms": 0.4,
"pattern_retrieval_us": 25,
"routing_accuracy_hybrid": 1.0,
"routing_accuracy_embedding_only": 0.45
},
"models": {
"claude_code_0.5b": {
"file": "ruvltra-claude-code-0.5b-q4_k_m.gguf",
"size_mb": 398,
"purpose": "Agent routing",
"context_length": 32768
},
"small_0.5b": {
"file": "ruvltra-small-0.5b-q4_k_m.gguf",
"size_mb": 400,
"purpose": "General embeddings",
"context_length": 32768
},
"medium_3b": {
"file": "ruvltra-medium-3b-q4_k_m.gguf",
"size_mb": 2048,
"purpose": "Full LLM inference",
"context_length": 262144
}
},
"performance_targets": {
"flash_attention_speedup": "2.49x-7.47x",
"hnsw_search_speedup": "150x-12500x",
"memory_reduction": "50-75%",
"mcp_response_ms": 100,
"sona_adaptation_ms": 0.05
},
"training_data": {
"labeled_examples": 381,
"contrastive_pairs": 793,
"agent_types": 60
}
} |