Instructions to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct",
	filename="Qwen-Qwen2.5-Coder-1.5B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct: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 migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct: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 migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M

Use Docker

docker model run hf.co/migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M

LM Studio
Jan

vLLM

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M

Ollama
How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with Ollama:
```
ollama run hf.co/migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
```

Unsloth Studio new

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct 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 migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct 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 migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct to start chatting

Pi new

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct: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": "Qwen-Qwen2.5-Coder-1.5B-Instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M
```

Lemonade

How to use migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull migarcoes/Qwen-Qwen2.5-Coder-1.5B-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.Qwen-Qwen2.5-Coder-1.5B-Instruct-Q4_K_M

List all available models

lemonade list

migarcoes commited on Mar 10

Commit

74f3027

verified ·

1 Parent(s): 0990ff0

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

README.md +10 -16

README.md CHANGED Viewed

@@ -1,25 +1,19 @@
 ---
-license: other
-license_name: qwen-license
-license_link: https://huggingface.co/Qwen/Qwen3.5-0.8B/blob/main/LICENSE
-base_model: Qwen/Qwen3.5-0.8B
-language:
-- en
-- zh
-pipeline_tag: text-generation
 tags:
-- llama.cpp
 - gguf
-- quantization
-- qwen
 ---
 # Qwen-Qwen2.5-Coder-1.5B-Instruct - GGUF
-Cuantizaciones GGUF para uso local.
-## Archivos incluidos:
-* **Q4_K_M.gguf**: Recomendado para balance entre precisión y rendimiento.
-* **f16.gguf**: Precisión completa para conversiones o pruebas de alta fidelidad.
-*Pendiente de benchmarks de rendimiento en hardware local.*

 ---
+license: apache-2.0
 tags:
 - gguf
+- quantized
+- text-generation
+base_model: Qwen-Qwen2.5-Coder-1.5B-Instruct
 ---
 # Qwen-Qwen2.5-Coder-1.5B-Instruct - GGUF
+Versiones cuantizadas del modelo **Qwen-Qwen2.5-Coder-1.5B-Instruct** para ejecución local eficiente.
+## Archivos disponibles:
+* **Qwen-Qwen2.5-Coder-1.5B-Instruct-Q4_K_M.gguf**: Balance óptimo entre peso y lógica (4-bit).
+* **Instruct-f16.gguf**: Alta fidelidad (16-bit) para máxima precisión.
+---
+*Nota: Estos modelos han sido procesados para su uso en hardware con recursos limitados (Linux/CPU/Mobile).*