Text Generation
GGUF
Rust
English
ruvllm
agent-routing
claude-code
recursive-language-model
embeddings
llm-inference
sona
hnsw
simd
imatrix
conversational
Instructions to use ruv/ruvltra 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 ruv/ruvltra 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 ruv/ruvltra:Q4_K_M # Run inference directly in the terminal: llama cli -hf ruv/ruvltra:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ruv/ruvltra:Q4_K_M # Run inference directly in the terminal: llama cli -hf ruv/ruvltra: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 ruv/ruvltra:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf ruv/ruvltra: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 ruv/ruvltra:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf ruv/ruvltra:Q4_K_M
Use Docker
docker model run hf.co/ruv/ruvltra:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use ruv/ruvltra with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ruv/ruvltra" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ruv/ruvltra", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ruv/ruvltra:Q4_K_M
- Ollama
How to use ruv/ruvltra with Ollama:
ollama run hf.co/ruv/ruvltra:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use ruv/ruvltra with Docker Model Runner:
docker model run hf.co/ruv/ruvltra:Q4_K_M
- Lemonade
How to use ruv/ruvltra with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ruv/ruvltra:Q4_K_M
Run and chat with the model
lemonade run user.ruvltra-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Commit History
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Calibration: benchmark_results.json
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Calibration: default.turboquant.json
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Add TurboQuant compatibility, v2.1.0 ecosystem tags
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Add TurboQuant compatibility and v2.1.0 ecosystem tags
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docs: Comprehensive Claude Code README with features and novel capabilities
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data: Add v2.5 performance optimization stats
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docs: Update model card for v2.5 - Performance Optimized Edition
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Update model card for v2.4 Ecosystem Edition with 100% routing accuracy
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Update v2.4 SOTA training stats
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v2.4 ecosystem training stats
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v2.4: Ecosystem Edition - 2,545 triplets, 388 capabilities, 100% validation
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Fix collapsed sections: use <b> instead of <h2> for inline formatting
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Add collapsed sections, SEO optimization, model-index metrics
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Update badges with SOTA metrics: 100% hybrid, 88.2% embedding, contrastive training
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Update model card with SOTA contrastive training results (88.2% embedding, 100% hybrid)
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Update model card with 100% routing accuracy benchmarks
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docs: remove redundant metrics and honest assessment sections
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docs: move use case table and honest assessment to top of page
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docs: add npx ruvector install and Rust crates documentation
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docs: focus on cost savings - $0 local routing vs $0.003/call API
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docs: remove misleading HumanEval benchmarks, add honest metrics assessment
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docs: honest positioning - augments Claude Code, doesn't replace it
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feat: add RuVector badge and self-learning flow diagram
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fix: summary tags now single-line (removed nested b/h3 tags)
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fix: restore collapsed sections (remove 'open' attribute from details tags)
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docs: improved introduction with clear explanation of self-learning and multi-agent optimization
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docs: comprehensive model card with tutorials, API reference, and benchmarks
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docs: clarify RuvLLM requires candle feature, add hardware requirements
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Enhanced README with capabilities, collapsed tutorials, benchmarks, and comprehensive documentation
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Comprehensive model card: World's First Self-Learning LLMs for Claude Code
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Upload tokenizer
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Upload RuvLTRA Medium 1.1B Q4_K_M
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Upload RuvLTRA Small 0.5B Q4_K_M
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Upload RuvLTRA Claude Code 0.5B Q4_K_M
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initial commit
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