Image-Text-to-Text
GGUF
gsq
rco
quantization
mixed-precision
veriloop
vision
multimodal
code
math
speculative-decoding
mtp
imatrix
conversational
Instructions to use tahaalam2009/VeriLoop-E2-GSQ-RCO-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 tahaalam2009/VeriLoop-E2-GSQ-RCO-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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S # Run inference directly in the terminal: llama cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S # Run inference directly in the terminal: llama cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S # Run inference directly in the terminal: ./llama-cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
Use Docker
docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
- LM Studio
- Jan
- vLLM
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tahaalam2009/VeriLoop-E2-GSQ-RCO-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": "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
- Ollama
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Ollama:
ollama run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
- Unsloth Desktop
- Pi
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
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": "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Docker Model Runner:
docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
- Lemonade
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
Run and chat with the model
lemonade run user.VeriLoop-E2-GSQ-RCO-GGUF-IQ2_S
List all available models
lemonade list
- Hermes Agent
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
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 "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S" \ --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"
Download eval_summary.json from tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 1.56 kB
-
https://huggingface.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF/resolve/91fdc32dbdee16fd001e284b28fed3a9c0b940d5/eval_summary.json
- Command line
-
hf download hf://tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF@91fdc32dbdee16fd001e284b28fed3a9c0b940d5/eval_summary.json
-
curl -L -o eval_summary.json https://huggingface.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF/resolve/91fdc32dbdee16fd001e284b28fed3a9c0b940d5/eval_summary.json
1.56 kB
| { | |
| "model_id": "tsinghua-sigs-robot-lab/VeriLoop-E2", | |
| "quantization": "GSQ-RCO-IQ3_XXS", | |
| "quant_type": "IQ3_XXS", | |
| "bpw": 3.03, | |
| "file_size_bytes": 10196595264, | |
| "file_size_gb": 10.196595264, | |
| "file_size_gib": 9.496319353580475, | |
| "tensors_total": 851, | |
| "tensors_matched": 851, | |
| "imatrix_chunks": 30, | |
| "imatrix_chunk_size": 512, | |
| "imatrix_dataset": "Domain-matched Code (The Stack/Python) + Math (GSM8K) + Physics reasoning", | |
| "eval_protocol": { | |
| "context_length": 2048, | |
| "batch_size": 512, | |
| "seed": 42, | |
| "threads": 16, | |
| "reference_model": "VeriLoop-E2-BF16.gguf (50.11 GiB)" | |
| }, | |
| "results": { | |
| "wikitext2": { | |
| "chunks": 8, | |
| "tokens": 16384, | |
| "bf16_ppl": 4.838206, | |
| "bf16_uncertainty": 0.119841, | |
| "quant_ppl": 5.265922, | |
| "quant_uncertainty": 0.135316, | |
| "ppl_ratio": 1.088404, | |
| "mean_kld": 0.113963, | |
| "kld_uncertainty": 0.003937, | |
| "same_top_p_percent": 87.39, | |
| "same_top_p_uncertainty": 0.367, | |
| "rms_delta_p_percent": 10.229, | |
| "log_ppl_correlation": 97.14 | |
| }, | |
| "codemath": { | |
| "chunks": 4, | |
| "tokens": 8192, | |
| "dataset": "GSM8K test split + OpenAI HumanEval", | |
| "bf16_ppl": 1.955413, | |
| "bf16_uncertainty": 0.048372, | |
| "quant_ppl": 2.136999, | |
| "quant_uncertainty": 0.056438, | |
| "ppl_ratio": 1.092863, | |
| "mean_kld": 0.114823, | |
| "kld_uncertainty": 0.005369, | |
| "same_top_p_percent": 90.885, | |
| "same_top_p_uncertainty": 0.45, | |
| "rms_delta_p_percent": 13.049, | |
| "log_ppl_correlation": 92.57 | |
| } | |
| } | |
| } |