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.md from bowmanslayer/VeriLoop-E2-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/bowmanslayer/VeriLoop-E2-GGUF/resolve/main/EVALUATION.md
- Command line
-
hf download hf://bowmanslayer/VeriLoop-E2-GGUF/EVALUATION.md
-
curl -L -o EVALUATION.md https://huggingface.co/bowmanslayer/VeriLoop-E2-GGUF/resolve/main/EVALUATION.md
5.91 kB
| # VeriLoop-E2 paired quantized evaluation | |
| Same frozen questions, seed 20260816, thinking enabled, publisher raw-content protocol, 32768-token output budget. Legacy custom scoring, including partial IFEval rules. Format comparisons use different engines/KV types; compare original versus ablated within each format. | |
| Runtime: dual RTX 3090. W4A16 uses vLLM 0.29.0 with a local FA2/FP8-KV plugin, tensor parallel 2, concurrency 16, 65,536-token context, FP8 E5M2 KV. GGUF uses llama.cpp layer split 1:1, concurrency 8, 40,960-token context per slot, Q8_0 key/value cache. Both use temperature 0 and top_p 1. See evaluation-runtime-versions.json for exact runtime identifiers. | |
| ## w4a16 | |
| | Task | n | Original | Ablated | Delta (pp) | | |
| |---|---:|---:|---:|---:| | |
| | mmlu | 150 | 84.00% | 76.00% | -8.00 | | |
| | cmmlu | 150 | 84.67% | 75.33% | -9.33 | | |
| | mmlu_pro | 150 | 78.67% | 70.67% | -8.00 | | |
| | ceval | 150 | 84.00% | 76.00% | -8.00 | | |
| | arc | 150 | 80.67% | 85.33% | +4.67 | | |
| | truthfulqa | 150 | 58.67% | 71.33% | +12.67 | | |
| | gsm8k | 100 | 95.00% | 88.00% | -7.00 | | |
| | math500 | 100 | 75.00% | 73.00% | -2.00 | | |
| | bbh | 150 | 75.33% | 65.33% | -10.00 | | |
| | humaneval | 164 | 89.63% | 89.63% | +0.00 | | |
| | ifeval | 100 | 76.00% | 69.00% | -7.00 | | |
| Overall: 1212/1514 (80.05%) → 1157/1514 (76.42%). Delta -3.63 pp, paired approximate 95% CI [-5.894, -1.371] pp. Lost/gained: 181/126. | |
| Empty: [9, 8]; explicitly delimited reasoning: [1186, 1007]; length-limited: [9, 8]. Empty-rate validity gate (≤2%): False. | |
| Refusal head markers (100 prompts, thinking off): [98, 0]; validity: [True, True]; 2048-token budget hits: [1, 9]. See comparison.json for all marker counts. | |
| Empty-rate gate exception: original-w4a16 / humaneval: 5/164 (3.05%), above 2%. These outputs remain failed in the denominator. | |
| ### Refusal marker diagnostics | |
| | Metric | Original | Ablated | | |
| |---|---:|---:| | |
| | substring_any | 98 | 2 | | |
| | substring_head | 98 | 0 | | |
| | heretic33_any | 98 | 69 | | |
| | ega26_any | 98 | 55 | | |
| | short | 0 | 0 | | |
| | empty | 0 | 0 | | |
| | empty_but_reasoning | 0 | 0 | | |
| | reasoning_leaked | 0 | 0 | | |
| | errors | 0 | 0 | | |
| Selection-independent 84-item subset: opening markers [82, 0]; anywhere markers [82, 2]. These are heuristic counts, not a semantic compliance score. | |
| ## gguf | |
| | Task | n | Original | Ablated | Delta (pp) | | |
| |---|---:|---:|---:|---:| | |
| | mmlu | 150 | 68.00% | 68.00% | +0.00 | | |
| | cmmlu | 150 | 61.33% | 64.67% | +3.33 | | |
| | mmlu_pro | 150 | 64.00% | 68.67% | +4.67 | | |
| | ceval | 150 | 60.00% | 70.00% | +10.00 | | |
| | arc | 150 | 90.00% | 91.33% | +1.33 | | |
| | truthfulqa | 150 | 60.67% | 60.67% | +0.00 | | |
| | gsm8k | 100 | 37.00% | 47.00% | +10.00 | | |
| | math500 | 100 | 68.00% | 67.00% | -1.00 | | |
| | bbh | 150 | 48.00% | 54.67% | +6.67 | | |
| | humaneval | 164 | 90.85% | 90.24% | -0.61 | | |
| | ifeval | 100 | 63.00% | 60.00% | -3.00 | | |
| Overall: 995/1514 (65.72%) → 1039/1514 (68.63%). Delta +2.91 pp, paired approximate 95% CI [0.956, 4.856] pp. Lost/gained: 92/136. | |
| Empty: [4, 7]; explicitly delimited reasoning: [741, 782]; length-limited: [4, 7]. Empty-rate validity gate (≤2%): True. | |
| Refusal head markers (100 prompts, thinking off): [99, 0]; validity: [True, True]; 2048-token budget hits: [0, 7]. See comparison.json for all marker counts. | |
| ### Refusal marker diagnostics | |
| | Metric | Original | Ablated | | |
| |---|---:|---:| | |
| | substring_any | 99 | 0 | | |
| | substring_head | 99 | 0 | | |
| | heretic33_any | 99 | 69 | | |
| | ega26_any | 99 | 54 | | |
| | short | 0 | 0 | | |
| | empty | 0 | 0 | | |
| | empty_but_reasoning | 0 | 0 | | |
| | reasoning_leaked | 0 | 0 | | |
| | errors | 0 | 0 | | |
| Selection-independent 84-item subset: opening markers [83, 0]; anywhere markers [83, 0]. These are heuristic counts, not a semantic compliance score. | |
| ## Limits | |
| An initial original-W4A16 preflight incorrectly enabled a Qwen3 reasoning parser that the publisher does not prescribe. It classified 33/150 direct answers as hidden reasoning. Removing the incompatible parser recovered all 33, with zero empty/length-limited outputs; streaming and nonstreaming probes passed. All reported runs keep thinking enabled and the original template, use publisher raw-content semantics, and extract final content after a closing think tag only when present. Unclosed length-limited output is not scored as an answer. | |
| These are local sampled benchmarks with the historical custom scorers, not official leaderboard scores. Truncated or empty outputs remain in the primary denominator. Marker absence does not prove semantic compliance. Refusal probes overlap the earlier 16-item selection set for the first 16 items; the remaining 84 are not used for candidate selection. No new ablation tuning was performed after these evaluations. No equivalence claim follows solely from a nonsignificant difference. | |
| GGUF protocol verification: the supplied standalone source template overrides an older embedded identity block. All 1,514 prompts and token sequences match the source tokenizer. llama.cpp echoes the opening think generation-prefix tag in content-only mode; the protocol adapter removes that framing before legacy scoring. Two streaming/nonstreaming probes passed. The partial GGUF first pass is excluded. Requests allow 7,200 seconds for the slower engine, while output budgets and scorers remain unchanged. | |
| The fixed-protocol GGUF run was paused to investigate low GSM8K accuracy. Two failed cases remained wrong with prompt caching disabled and with CPU-only F16 KV. One also failed under the original Hugging Face BF16 model in Transformers. Sampled BF16 GGUF tensors matched the source exactly. These checks did not establish a GGUF-only pipeline fault; they do not establish full BF16 benchmark accuracy or explain the entire cross-format gap. The existing run resumed without changing questions, scoring, weights, or engine. Wall-clock durations include the diagnostic pause and are not throughput measurements. An isolated newer llama.cpp CPU build was not used for the reported evaluation. | |