Instructions to use Reza2kn/Bev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Reza2kn/Bev 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 Reza2kn/Bev:Q2_0
# Run inference directly in the terminal:
llama cli -hf Reza2kn/Bev:Q2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Reza2kn/Bev:Q2_0
# Run inference directly in the terminal:
llama cli -hf Reza2kn/Bev:Q2_0

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 Reza2kn/Bev:Q2_0
# Run inference directly in the terminal:
./llama-cli -hf Reza2kn/Bev:Q2_0

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 Reza2kn/Bev:Q2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Reza2kn/Bev:Q2_0

Use Docker

docker model run hf.co/Reza2kn/Bev:Q2_0

LM Studio
Jan

vLLM

How to use Reza2kn/Bev with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Reza2kn/Bev"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Reza2kn/Bev",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Reza2kn/Bev:Q2_0

Ollama
How to use Reza2kn/Bev with Ollama:
```
ollama run hf.co/Reza2kn/Bev:Q2_0
```
Unsloth Desktop

How to use Reza2kn/Bev with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Reza2kn/Bev:Q2_0

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": "Reza2kn/Bev:Q2_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Reza2kn/Bev with Docker Model Runner:
```
docker model run hf.co/Reza2kn/Bev:Q2_0
```

Lemonade

How to use Reza2kn/Bev with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Reza2kn/Bev:Q2_0

Run and chat with the model

lemonade run user.Bev-Q2_0

List all available models

lemonade list

Hermes Agent

How to use Reza2kn/Bev with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Reza2kn/Bev:Q2_0

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 Reza2kn/Bev:Q2_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Reza2kn/Bev with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Reza2kn/Bev:Q2_0

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 "Reza2kn/Bev:Q2_0" \
  --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"

Bev

File size: 981 Bytes

f01edf9

{
  "date_utc": "2026-09-23",
  "host": "Stallion, Linux x86_64",
  "runtime_commit": "9a9394a895b96003ca842a6041cb28ac49a108f7",
  "model_sha256": "3907dc1658db1f78a9826bf8d5bcb8dc65db0d466388937af57f2294fae62ec1",
  "backend": "CPU only, n_gpu_layers=0",
  "context_total_tokens": 4096,
  "parallel_slots": 1,
  "kv_type": "q8_0",
  "request": "One short /completion request with selected_token_ids=[1,2] and n_predict=1; response returned HTTP 200 with one token.",
  "loaded_idle_vm_rss_kib": 7468732,
  "post_inference_vm_hwm_kib": 7630416,
  "post_inference_vm_rss_kib": 7630416,
  "post_inference_pss_kib": 7626623,
  "post_inference_pss_file_kib": 7037583,
  "post_inference_pss_anon_kib": 589040,
  "measurement_source": "/proc/<owned Bev server PID>/status and smaps_rollup, immediately after the request",
  "interpretation": "RSS includes file-backed resident model pages; this is an observed run, not a general minimum or controlled peak across arbitrary requests."
}