Instructions to use tel1980/qwen-python-slm-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 tel1980/qwen-python-slm-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 tel1980/qwen-python-slm-gguf
# Run inference directly in the terminal:
llama cli -hf tel1980/qwen-python-slm-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tel1980/qwen-python-slm-gguf
# Run inference directly in the terminal:
llama cli -hf tel1980/qwen-python-slm-gguf

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 tel1980/qwen-python-slm-gguf
# Run inference directly in the terminal:
./llama-cli -hf tel1980/qwen-python-slm-gguf

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 tel1980/qwen-python-slm-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tel1980/qwen-python-slm-gguf

Use Docker

docker model run hf.co/tel1980/qwen-python-slm-gguf

LM Studio
Jan
Ollama
How to use tel1980/qwen-python-slm-gguf with Ollama:
```
ollama run hf.co/tel1980/qwen-python-slm-gguf
```

Unsloth Studio

How to use tel1980/qwen-python-slm-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 tel1980/qwen-python-slm-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 tel1980/qwen-python-slm-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tel1980/qwen-python-slm-gguf to start chatting

How to use tel1980/qwen-python-slm-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tel1980/qwen-python-slm-gguf

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": "tel1980/qwen-python-slm-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tel1980/qwen-python-slm-gguf with Docker Model Runner:
```
docker model run hf.co/tel1980/qwen-python-slm-gguf
```

Lemonade

How to use tel1980/qwen-python-slm-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tel1980/qwen-python-slm-gguf

Run and chat with the model

lemonade run user.qwen-python-slm-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use tel1980/qwen-python-slm-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 tel1980/qwen-python-slm-gguf

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 tel1980/qwen-python-slm-gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tel1980/qwen-python-slm-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tel1980/qwen-python-slm-gguf

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 "tel1980/qwen-python-slm-gguf" \
  --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"

qwen-python-slm-gguf

File size: 1,036 Bytes

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---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
  - code
  - python
  - sql
  - pyspark
  - gguf
  - ollama
  - llama.cpp
---

# qwen-python-slm (GGUF)

GGUF quantizado em **Q4_K_M** (941 MB) do [`tel1980/qwen-python-slm`](https://huggingface.co/tel1980/qwen-python-slm) — SLM de 1,5B especializado em gerar código Python, SQL e PySpark para Engenharia de Dados, Data Science, ML/LLMs, Agentes de IA e MLOps.

Model card completo, métricas de avaliação e detalhes de treino: veja o [repositório principal](https://huggingface.co/tel1980/qwen-python-slm).

## Uso com Ollama

```bash
ollama run hf.co/tel1980/qwen-python-slm-gguf
```

Prefixos de linguagem reconhecidos: `/python`, `/sql`, `/pyspark`, `/ml`, `/llm`, `/agent`, `/dataops`.

```bash
ollama run hf.co/tel1980/qwen-python-slm-gguf "/sql Liste os 10 produtos mais vendidos no último trimestre."
```

## Uso com llama.cpp

```bash
llama-cli -m qwen2.5-coder-1.5b-python-q4.gguf -p "/python Crie uma função que leia um CSV com pandas."
```