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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Denisijcu/vertexqwen 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 Denisijcu/vertexqwen:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Denisijcu/vertexqwen:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Denisijcu/vertexqwen:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Denisijcu/vertexqwen:Q4_K_M

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 Denisijcu/vertexqwen:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Denisijcu/vertexqwen:Q4_K_M

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 Denisijcu/vertexqwen:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Denisijcu/vertexqwen:Q4_K_M

Use Docker

docker model run hf.co/Denisijcu/vertexqwen:Q4_K_M

LM Studio
Jan
Ollama
How to use Denisijcu/vertexqwen with Ollama:
```
ollama run hf.co/Denisijcu/vertexqwen:Q4_K_M
```
Unsloth Desktop

How to use Denisijcu/vertexqwen with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertexqwen:Q4_K_M

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": "Denisijcu/vertexqwen:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Denisijcu/vertexqwen with Docker Model Runner:
```
docker model run hf.co/Denisijcu/vertexqwen:Q4_K_M
```

Lemonade

How to use Denisijcu/vertexqwen with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Denisijcu/vertexqwen:Q4_K_M

Run and chat with the model

lemonade run user.vertexqwen-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Denisijcu/vertexqwen with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertexqwen:Q4_K_M

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 Denisijcu/vertexqwen:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Denisijcu/vertexqwen with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertexqwen:Q4_K_M

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 "Denisijcu/vertexqwen:Q4_K_M" \
  --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"

Vertex-Qwen Chess (3B QLoRA)

Vertex-Qwen Chess is a fine-tuned tactical chess engine based on Qwen 2.5 (3B-Instruct), developed by Vertex Coders LLC (Miami, FL). It is trained to act as an autonomous grandmaster-level reasoning agent capable of interpreting board states in FEN notation, evaluating legal moves in SAN/UCI, and executing high-precision tactical play.

Model Details

Developer: Vertex Coders LLC (Denis Sanchez Leyva)
Base Model: Qwen/Qwen2.5-3B-Instruct
Fine-tuning Framework: Unsloth QLoRA (Local workstation: NVIDIA GTX 1660 Ti, 6GB VRAM)
Dataset: Grandmaster Blend (5,000 elite chess positions and tactical sequences)
Training Hyperparameters:
- Epochs: 1
- Total Steps: 150 steps
- Effective Batch Size: 8 (Batch per device: 1, Gradient Accumulation: 8)
- Precision: FP16 / LoRA rank ($r=8$)

Intended Use

Designed to run locally via Ollama or custom FastAPI inference arbiters within the Vertex Chess Arena platform. It excels at legal move generation, tactical openings, and structural middle-game calculations.

How to Get Started

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "your-huggingface-username/vertex-qwen-chess"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")

Downloads last month: 9

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for Denisijcu/vertexqwen

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