Instructions to use mengleap-stnap/Qwenseek-coding-Merged-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 mengleap-stnap/Qwenseek-coding-Merged-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 mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF: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 mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF: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 mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with Ollama:
```
ollama run hf.co/mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
```

Unsloth Studio

How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mengleap-stnap/Qwenseek-coding-Merged-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for mengleap-stnap/Qwenseek-coding-Merged-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mengleap-stnap/Qwenseek-coding-Merged-GGUF to start chatting

How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mengleap-stnap/Qwenseek-coding-Merged-GGUF: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 "mengleap-stnap/Qwenseek-coding-Merged-GGUF: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"

Docker Model Runner
How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with Docker Model Runner:
```
docker model run hf.co/mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M
```

Lemonade

How to use mengleap-stnap/Qwenseek-coding-Merged-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwenseek-coding-Merged-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mengleap-stnap/Qwenseek-coding-Merged-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 mengleap-stnap/Qwenseek-coding-Merged-GGUF: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 mengleap-stnap/Qwenseek-coding-Merged-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Qwenseek-Coding-Merged-1.5B

Qwenseek-Coding-Merged-1.5B is a merged model combining the robust code-generation capabilities of Qwen2.5-Coder-1.5B-Instruct with the deep reasoning and logic skills of DeepSeek-R1-Distill-Qwen-1.5B. It was merged using the SLERP (Spherical Linear Interpolation) method via mergekit.

This model is engineered to achieve a sweet spot between accurate code syntax generation and deep reasoning logic for bug fixing.

Model Capabilities & Benchmarks

Key improvements achieved through this merge:

Logic & Bug Fixing: Significantly enhanced compared to the original Qwen2.5-Coder, thanks to DeepSeek-R1's reasoning distillation.
Coding Syntax & Multi-language: Preserves strong code generation accuracy without performance degradation.
Efficiency: At only 1.5B parameters, it runs extremely fast and requires minimal memory/VRAM.

Merge Configuration

The merge was executed using mergekit with the following configuration:

slices:
  - sources:
      - model: Qwen/Qwen2.5-Coder-1.5B-Instruct
        layer_range: [0, 28]
        weight: 0.6
      - model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
        layer_range: [0, 28]
        weight: 0.4
merge_method: slerp
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Downloads last month: 118

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

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