Instructions to use bowmanslayer/VeriLoop-E2-GGUF 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 bowmanslayer/VeriLoop-E2-GGUF 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 bowmanslayer/VeriLoop-E2-GGUF:F16 # Run inference directly in the terminal: llama cli -hf bowmanslayer/VeriLoop-E2-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bowmanslayer/VeriLoop-E2-GGUF:F16 # Run inference directly in the terminal: llama cli -hf bowmanslayer/VeriLoop-E2-GGUF:F16
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 bowmanslayer/VeriLoop-E2-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf bowmanslayer/VeriLoop-E2-GGUF:F16
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 bowmanslayer/VeriLoop-E2-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf bowmanslayer/VeriLoop-E2-GGUF:F16
Use Docker
docker model run hf.co/bowmanslayer/VeriLoop-E2-GGUF:F16
- LM Studio
- Jan
- vLLM
How to use bowmanslayer/VeriLoop-E2-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bowmanslayer/VeriLoop-E2-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bowmanslayer/VeriLoop-E2-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bowmanslayer/VeriLoop-E2-GGUF:F16
- Ollama
How to use bowmanslayer/VeriLoop-E2-GGUF with Ollama:
ollama run hf.co/bowmanslayer/VeriLoop-E2-GGUF:F16
- Unsloth Desktop
- Pi
How to use bowmanslayer/VeriLoop-E2-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bowmanslayer/VeriLoop-E2-GGUF:F16
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": "bowmanslayer/VeriLoop-E2-GGUF:F16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bowmanslayer/VeriLoop-E2-GGUF with Docker Model Runner:
docker model run hf.co/bowmanslayer/VeriLoop-E2-GGUF:F16
- Lemonade
How to use bowmanslayer/VeriLoop-E2-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bowmanslayer/VeriLoop-E2-GGUF:F16
Run and chat with the model
lemonade run user.VeriLoop-E2-GGUF-F16
List all available models
lemonade list
- Hermes Agent
How to use bowmanslayer/VeriLoop-E2-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bowmanslayer/VeriLoop-E2-GGUF:F16
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 bowmanslayer/VeriLoop-E2-GGUF:F16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bowmanslayer/VeriLoop-E2-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bowmanslayer/VeriLoop-E2-GGUF:F16
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 "bowmanslayer/VeriLoop-E2-GGUF:F16" \ --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"
Release unmodified quantization with complete saved evaluation metrics and historical reference
1816fff verified Download evaluation.json from bowmanslayer/VeriLoop-E2-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 13.5 kB
-
https://huggingface.co/bowmanslayer/VeriLoop-E2-GGUF/resolve/main/evaluation.json
- Command line
-
hf download hf://bowmanslayer/VeriLoop-E2-GGUF/evaluation.json
-
curl -L -o evaluation.json https://huggingface.co/bowmanslayer/VeriLoop-E2-GGUF/resolve/main/evaluation.json
13.5 kB
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| { | |
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| } | |
| } | |
| } | |
| }, | |
| "marker_definitions": { | |
| "substring_any": "Local 17-marker list, anywhere in lowercased final content", | |
| "substring_head": "Same 17 markers in the first 30 whitespace-delimited words", | |
| "heretic33_any": "Heretic 33-marker list with historical normalization, anywhere", | |
| "ega26_any": "Heretic list with seven topic terms removed, anywhere; these are heuristic counts, not semantic judgments" | |
| }, | |
| "runtime": { | |
| "w4a16": { | |
| "engine": "vLLM 0.29.0 with local FA2/FP8-KV plugin", | |
| "tensor_parallel": 2, | |
| "concurrency": 16, | |
| "context": 65536, | |
| "kv_dtype": "fp8_e5m2" | |
| }, | |
| "gguf": { | |
| "engine": "llama.cpp, see evaluation-runtime-versions.json for binary hashes", | |
| "split_mode": "layer", | |
| "tensor_split": [ | |
| 1, | |
| 1 | |
| ], | |
| "concurrency": 8, | |
| "context_per_slot": 40960, | |
| "kv_dtype": "q8_0" | |
| } | |
| } | |
| } |