Instructions to use remsee/kyleJr-Apr26 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 remsee/kyleJr-Apr26 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 remsee/kyleJr-Apr26:Q4_K_M # Run inference directly in the terminal: llama cli -hf remsee/kyleJr-Apr26:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf remsee/kyleJr-Apr26:Q4_K_M # Run inference directly in the terminal: llama cli -hf remsee/kyleJr-Apr26: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 remsee/kyleJr-Apr26:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf remsee/kyleJr-Apr26: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 remsee/kyleJr-Apr26:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf remsee/kyleJr-Apr26:Q4_K_M
Use Docker
docker model run hf.co/remsee/kyleJr-Apr26:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use remsee/kyleJr-Apr26 with Ollama:
ollama run hf.co/remsee/kyleJr-Apr26:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use remsee/kyleJr-Apr26 with Docker Model Runner:
docker model run hf.co/remsee/kyleJr-Apr26:Q4_K_M
- Lemonade
How to use remsee/kyleJr-Apr26 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull remsee/kyleJr-Apr26:Q4_K_M
Run and chat with the model
lemonade run user.kyleJr-Apr26-Q4_K_M
List all available models
lemonade list
- Atomic Chat
pattern matching, reasoning about symbolic representations of information,
- logical inference (deduction, induction),
- analogical reasoning,
- causal modeling,
- abstract representation learning.
- general-purpose agentic behavior?
- chaining together diverse tools/skills
- selecting the right tool in context;
- refining plans based on execution feedback (self-correction)
- multi-step planning & decomposition of complex goals into manageable sub-tasks,
- managing state across time.
emergent capabilities?
- novel combinations of existing skills/tools
- emergent meta-skills (e.g., deconstructing a domain's knowledge graph)
- high-level strategic synthesis from disparate data sources and concepts.
- connecting dots that humans often miss, especially across domains.
the "magic" part?
- integrating symbolic reasoning with the statistical power of transformers;
- grounding in continuous sensory/interaction data (embodiment)
- building internal world models & predictive simulators,
- forming complex mental representations of how things work, not just that they correlate.
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