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 14 days ago

Commit

91fdc32

verified ·

1 Parent(s): 6ef93be

Upload eval_summary.json with huggingface_hub

Browse files

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eval_summary.json +54 -0

eval_summary.json ADDED Viewed

	@@ -0,0 +1,54 @@

+{
+  "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
+    }
+  }
+}