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

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

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"

tahaalam2009 commited on 16 days ago

Commit

840bdf8

verified ·

1 Parent(s): 9611925

Upload eval_summary_deep_dive.json with huggingface_hub

Browse files

Files changed (1) hide show

eval_summary_deep_dive.json +120 -0

eval_summary_deep_dive.json ADDED Viewed

	@@ -0,0 +1,120 @@

+{
+  "benchmarks": {
+    "AIME2025": {
+      "metric": "Accuracy / Retention (%)",
+      "Qwen_BF16": 100.0,
+      "Qwen_IQ3_XXS": 100.0,
+      "Qwen_IQ3_S": 100.0,
+      "VeriLoop_BF16": 100.0,
+      "VeriLoop_IQ3_XXS": 100.0,
+      "VeriLoop_IQ3_S": 100.0
+    },
+    "GPQA_Diamond": {
+      "metric": "Accuracy / Retention (%)",
+      "Qwen_BF16": 89.9,
+      "Qwen_IQ3_XXS": 88.89,
+      "Qwen_IQ3_S": 89.39,
+      "VeriLoop_BF16": 90.2,
+      "VeriLoop_IQ3_XXS": 89.38,
+      "VeriLoop_IQ3_S": 90.05
+    },
+    "LiveCodeBench_v6": {
+      "metric": "Pass@1 / Retention (%)",
+      "Qwen_BF16": 85.71,
+      "Qwen_IQ3_XXS": 84.57,
+      "Qwen_IQ3_S": 85.71,
+      "VeriLoop_BF16": 86.4,
+      "VeriLoop_IQ3_XXS": 85.45,
+      "VeriLoop_IQ3_S": 86.4
+    },
+    "TerminalBench_2_1": {
+      "metric": "Task Completion / Retention (%)",
+      "Qwen_BF16": 82.5,
+      "Qwen_IQ3_XXS": 81.68,
+      "Qwen_IQ3_S": 82.5,
+      "VeriLoop_BF16": 88.6,
+      "VeriLoop_IQ3_XXS": 88.1,
+      "VeriLoop_IQ3_S": 88.7
+    },
+    "WikiText2": {
+      "metric": "Perplexity (Lower is Better)",
+      "Qwen_BF16": 7.05,
+      "Qwen_IQ3_XXS": 7.2,
+      "Qwen_IQ3_S": 7.07,
+      "VeriLoop_BF16": 4.838,
+      "VeriLoop_IQ3_XXS": 5.266,
+      "VeriLoop_IQ3_S": 4.978
+    },
+    "Code_Math": {
+      "metric": "Perplexity (Lower is Better)",
+      "Qwen_BF16": 2.85,
+      "Qwen_IQ3_XXS": 3.08,
+      "Qwen_IQ3_S": 2.91,
+      "VeriLoop_BF16": 1.955,
+      "VeriLoop_IQ3_XXS": 2.137,
+      "VeriLoop_IQ3_S": 2.012
+    }
+  },
+  "fidelity": {
+    "WikiText2": {
+      "Top1_Agreement": {
+        "Qwen_IQ3_XXS": 85.5,
+        "Qwen_IQ3_S": 91.2,
+        "VeriLoop_IQ3_XXS": 87.39,
+        "VeriLoop_IQ3_S": 92.85
+      },
+      "Top5_Agreement": {
+        "Qwen_IQ3_XXS": 96.8,
+        "Qwen_IQ3_S": 98.4,
+        "VeriLoop_IQ3_XXS": 97.42,
+        "VeriLoop_IQ3_S": 98.92
+      },
+      "Mean_KLD": {
+        "Qwen_IQ3_XXS": 0.118,
+        "Qwen_IQ3_S": 0.042,
+        "VeriLoop_IQ3_XXS": 0.114,
+        "VeriLoop_IQ3_S": 0.0385
+      }
+    },
+    "Code_Math": {
+      "Top1_Agreement": {
+        "Qwen_IQ3_XXS": 87.2,
+        "Qwen_IQ3_S": 93.8,
+        "VeriLoop_IQ3_XXS": 90.89,
+        "VeriLoop_IQ3_S": 95.12
+      },
+      "Top5_Agreement": {
+        "Qwen_IQ3_XXS": 97.4,
+        "Qwen_IQ3_S": 98.9,
+        "VeriLoop_IQ3_XXS": 98.24,
+        "VeriLoop_IQ3_S": 99.41
+      },
+      "Mean_KLD": {
+        "Qwen_IQ3_XXS": 0.112,
+        "Qwen_IQ3_S": 0.039,
+        "VeriLoop_IQ3_XXS": 0.1148,
+        "VeriLoop_IQ3_S": 0.0362
+      }
+    },
+    "TerminalBench_2_1": {
+      "Top1_Agreement": {
+        "Qwen_IQ3_XXS": 88.1,
+        "Qwen_IQ3_S": 94.2,
+        "VeriLoop_IQ3_XXS": 91.52,
+        "VeriLoop_IQ3_S": 95.84
+      },
+      "Top5_Agreement": {
+        "Qwen_IQ3_XXS": 97.8,
+        "Qwen_IQ3_S": 99.1,
+        "VeriLoop_IQ3_XXS": 98.65,
+        "VeriLoop_IQ3_S": 99.55
+      },
+      "Mean_KLD": {
+        "Qwen_IQ3_XXS": 0.104,
+        "Qwen_IQ3_S": 0.035,
+        "VeriLoop_IQ3_XXS": 0.0982,
+        "VeriLoop_IQ3_S": 0.0314
+      }
+    }
+  }
+}