Instructions to use Esobold/EsoTest_GGUFs 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 Esobold/EsoTest_GGUFs 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 Esobold/EsoTest_GGUFs:Q4_K_M # Run inference directly in the terminal: llama cli -hf Esobold/EsoTest_GGUFs:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Esobold/EsoTest_GGUFs:Q4_K_M # Run inference directly in the terminal: llama cli -hf Esobold/EsoTest_GGUFs: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 Esobold/EsoTest_GGUFs:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Esobold/EsoTest_GGUFs: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 Esobold/EsoTest_GGUFs:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Esobold/EsoTest_GGUFs:Q4_K_M
Use Docker
docker model run hf.co/Esobold/EsoTest_GGUFs:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Esobold/EsoTest_GGUFs with Ollama:
ollama run hf.co/Esobold/EsoTest_GGUFs:Q4_K_M
- Unsloth Desktop
- Pi
How to use Esobold/EsoTest_GGUFs with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Esobold/EsoTest_GGUFs:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Esobold/EsoTest_GGUFs:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Esobold/EsoTest_GGUFs with Docker Model Runner:
docker model run hf.co/Esobold/EsoTest_GGUFs:Q4_K_M
- Lemonade
How to use Esobold/EsoTest_GGUFs with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Esobold/EsoTest_GGUFs:Q4_K_M
Run and chat with the model
lemonade run user.EsoTest_GGUFs-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Esobold/EsoTest_GGUFs with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Esobold/EsoTest_GGUFs:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default Esobold/EsoTest_GGUFs:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Esobold/EsoTest_GGUFs with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Esobold/EsoTest_GGUFs:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Esobold/EsoTest_GGUFs:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Commit History
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Gemma4-TypoArcheopterix: nearswap order-test, base=TropicalPterodactyl(rebuilt) + nbeerbower Gutenberg, t=[0,0,0.0001] — sha256:0d31f0f27b80ca7b
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Gemma4-TypoPterodactyl: nearswap order-test, base=TropicalPterodactyl(rebuilt) + nbeerbower Gutenberg, t=[0,0,0.0001] — sha256:50ff1ca6834cd75b
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meshcore_path_resolver.py - stdlib-only packet path resolver with origin estimation (python3, no deps)
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g4-31b DPO round2 (base + G1b + G2 adapters, r32 a64 x2) — sha256:ffcc5794ef4331d9
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PrehistoricVolcano nearswap (EquidistantGH x Scotoma-2; order-of-ops test) — sha256:2733f2d7d27b6869
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MiMo 9B S3half r9-merged (DPO-9 baked, style-tuned) — --no-mtp build — sha256:0412792cfaa82dd2
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rebuild with --no-mtp (complete metadata fix; block_count 32, recurrent_layers 32) — sha256:76367f5de3fc87eb
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rebuild with --no-mtp (fixes phantom nextn metadata; recurrent_layers=32) — sha256:8892cbf39900971d
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MiMo 9B S3-half + round8 DPO (175 accumulated pairs) — repetition fixed — sha256:57c0c45cc58b8dde
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VolcanicPterodactyl nearswap (TropicalPterodactyl x Equinox-31B) — sha256:a4c046ff444f810c
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Add SpotToDig Nearswap Q4_K_M GGUF
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README: add GhostShip entry
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GhostShip: FM nearswap Heretic<-scotoma-2, t 0.0001->0.0007 linear (avg 4e-4), Q4_K_M
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gemma-4-31b nearswap MoonRocks: Garnet-V2 base + scotoma-2, flat t=0.0002, Q4_K_M
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gemma-4-31b nearswap merge: Garnet-V2 base + scotoma-2 (t=0.0001), Q4_K_M
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