Instructions to use lschaffer/qwen25-3b-tealkit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lschaffer/qwen25-3b-tealkit with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lschaffer/qwen25-3b-tealkit",
	filename="qwen25-3b-tealkit-q5_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 lschaffer/qwen25-3b-tealkit with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lschaffer/qwen25-3b-tealkit:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lschaffer/qwen25-3b-tealkit:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lschaffer/qwen25-3b-tealkit:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf lschaffer/qwen25-3b-tealkit:Q5_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 lschaffer/qwen25-3b-tealkit:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf lschaffer/qwen25-3b-tealkit:Q5_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 lschaffer/qwen25-3b-tealkit:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lschaffer/qwen25-3b-tealkit:Q5_K_M

Use Docker

docker model run hf.co/lschaffer/qwen25-3b-tealkit:Q5_K_M

LM Studio
Jan

vLLM

How to use lschaffer/qwen25-3b-tealkit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lschaffer/qwen25-3b-tealkit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lschaffer/qwen25-3b-tealkit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lschaffer/qwen25-3b-tealkit:Q5_K_M

Ollama
How to use lschaffer/qwen25-3b-tealkit with Ollama:
```
ollama run hf.co/lschaffer/qwen25-3b-tealkit:Q5_K_M
```

Unsloth Studio new

How to use lschaffer/qwen25-3b-tealkit 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 lschaffer/qwen25-3b-tealkit 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 lschaffer/qwen25-3b-tealkit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lschaffer/qwen25-3b-tealkit to start chatting

Pi new

How to use lschaffer/qwen25-3b-tealkit with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lschaffer/qwen25-3b-tealkit:Q5_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": "lschaffer/qwen25-3b-tealkit:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lschaffer/qwen25-3b-tealkit with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf lschaffer/qwen25-3b-tealkit:Q5_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 lschaffer/qwen25-3b-tealkit:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use lschaffer/qwen25-3b-tealkit with Docker Model Runner:
```
docker model run hf.co/lschaffer/qwen25-3b-tealkit:Q5_K_M
```

Lemonade

How to use lschaffer/qwen25-3b-tealkit with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lschaffer/qwen25-3b-tealkit:Q5_K_M

Run and chat with the model

lemonade run user.qwen25-3b-tealkit-Q5_K_M

List all available models

lemonade list

qwen25-3b-tealkit / Modelfile

lschaffer

Upload Modelfile with huggingface_hub

e1bb698 verified 17 days ago

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463 Bytes

	FROM qwen25-3b-tealkit-q5_k_m.gguf
	# System prompt to trigger the tool-calling behavior learned in training
	SYSTEM "You are a specialized MCP Agent. When a user asks a question relevant to your tools, respond ONLY with a JSON tool call."
	SYSTEM "Output format must be exactly: tool_call: {\"name\": \"<tool_name>\", \"arguments\": { ... }}. No prose."
	# Limit context window to reduce KV-cache memory pressure on Mac
	PARAMETER num_ctx 1024
	PARAMETER num_thread 4