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)

GGUF quantizado em Q4_K_M (941 MB) do 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.

Uso com Ollama

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

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

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

Uso com llama.cpp

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

Downloads last month: 178

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for tel1980/qwen-python-slm-gguf

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B-Instruct

Quantized

(168)

this model