Instructions to use teleprint-me/qwen2.5-coder-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teleprint-me/qwen2.5-coder-instruct with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="teleprint-me/qwen2.5-coder-instruct",
	filename="qwen2.5-coder-1.5b-instruct-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use teleprint-me/qwen2.5-coder-instruct with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teleprint-me/qwen2.5-coder-instruct:BF16
# Run inference directly in the terminal:
llama-cli -hf teleprint-me/qwen2.5-coder-instruct:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf teleprint-me/qwen2.5-coder-instruct:BF16
# Run inference directly in the terminal:
llama-cli -hf teleprint-me/qwen2.5-coder-instruct:BF16

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 teleprint-me/qwen2.5-coder-instruct:BF16
# Run inference directly in the terminal:
./llama-cli -hf teleprint-me/qwen2.5-coder-instruct:BF16

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 teleprint-me/qwen2.5-coder-instruct:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf teleprint-me/qwen2.5-coder-instruct:BF16

Use Docker

docker model run hf.co/teleprint-me/qwen2.5-coder-instruct:BF16

LM Studio
Jan
Ollama
How to use teleprint-me/qwen2.5-coder-instruct with Ollama:
```
ollama run hf.co/teleprint-me/qwen2.5-coder-instruct:BF16
```

Unsloth Studio new

How to use teleprint-me/qwen2.5-coder-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 teleprint-me/qwen2.5-coder-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 teleprint-me/qwen2.5-coder-instruct to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for teleprint-me/qwen2.5-coder-instruct to start chatting

Pi new

How to use teleprint-me/qwen2.5-coder-instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teleprint-me/qwen2.5-coder-instruct:BF16

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": "teleprint-me/qwen2.5-coder-instruct:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use teleprint-me/qwen2.5-coder-instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf teleprint-me/qwen2.5-coder-instruct:BF16

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 teleprint-me/qwen2.5-coder-instruct:BF16

Run Hermes

hermes

Docker Model Runner
How to use teleprint-me/qwen2.5-coder-instruct with Docker Model Runner:
```
docker model run hf.co/teleprint-me/qwen2.5-coder-instruct:BF16
```

Lemonade

How to use teleprint-me/qwen2.5-coder-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull teleprint-me/qwen2.5-coder-instruct:BF16

Run and chat with the model

lemonade run user.qwen2.5-coder-instruct-BF16

List all available models

lemonade list

Qwen2.5 Coder Instruct

Original model weights.
Includes sizes for 1.5b, 3b, and 7b.
Converted from the original vendored weights to gguf as bf16.

Downloads last month: 9

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

16-bit

Inference Providers NEW

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

Model tree for teleprint-me/qwen2.5-coder-instruct

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-Coder-1.5B

Finetuned

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

Quantized

(92)

this model