Instructions to use lschaffer/qwen2_5_1_5b-webcrawl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lschaffer/qwen2_5_1_5b-webcrawl with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lschaffer/qwen2_5_1_5b-webcrawl",
	filename="qwen25-1p5b-webcrawl-q5_k_m.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lschaffer/qwen2_5_1_5b-webcrawl 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 lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf lschaffer/qwen2_5_1_5b-webcrawl: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/qwen2_5_1_5b-webcrawl:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf lschaffer/qwen2_5_1_5b-webcrawl: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/qwen2_5_1_5b-webcrawl:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

Use Docker

docker model run hf.co/lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

LM Studio
Jan

vLLM

How to use lschaffer/qwen2_5_1_5b-webcrawl with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lschaffer/qwen2_5_1_5b-webcrawl"
# 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/qwen2_5_1_5b-webcrawl",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

Ollama
How to use lschaffer/qwen2_5_1_5b-webcrawl with Ollama:
```
ollama run hf.co/lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M
```

Unsloth Studio

How to use lschaffer/qwen2_5_1_5b-webcrawl 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/qwen2_5_1_5b-webcrawl 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/qwen2_5_1_5b-webcrawl to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lschaffer/qwen2_5_1_5b-webcrawl to start chatting

How to use lschaffer/qwen2_5_1_5b-webcrawl with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lschaffer/qwen2_5_1_5b-webcrawl: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/qwen2_5_1_5b-webcrawl:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use lschaffer/qwen2_5_1_5b-webcrawl with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lschaffer/qwen2_5_1_5b-webcrawl: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/qwen2_5_1_5b-webcrawl:Q5_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use lschaffer/qwen2_5_1_5b-webcrawl with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

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 "lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M" \
  --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"

Docker Model Runner
How to use lschaffer/qwen2_5_1_5b-webcrawl with Docker Model Runner:
```
docker model run hf.co/lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M
```

Lemonade

How to use lschaffer/qwen2_5_1_5b-webcrawl with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lschaffer/qwen2_5_1_5b-webcrawl:Q5_K_M

Run and chat with the model

lemonade run user.qwen2_5_1_5b-webcrawl-Q5_K_M

List all available models

lemonade list

qwen25-1p5b-webcrawl

⚠️ This model is trained for the webcrawl MCP server. It is optimised for structured MCP tool-call generation and is not intended to be a general-purpose assistant.

GGUF model fine-tuned for structured MCP tool-calling, ready for local inference via Ollama.

Model Details

Property	Value
MCP server	webcrawl
Base model (training)	mlx-community/Qwen2.5-1.5B-Instruct-4bit
Base model (fused export)	Qwen/Qwen2.5-1.5B-Instruct
Fine-tune method	QLoRA / LoRA adapter fusion
Quantization	Q5_K_M (5-bit, recommended)
GGUF file	qwen25-1p5b-webcrawl-q5_k_m.gguf
Preset	qwen2_5_1_5b

Intended Use

Intended for agents or apps that call the webcrawl MCP server.

The model was trained on a custom MCP tool-call JSONL dataset derived from the server's tool schema. It is intended to emit structured JSON tool calls for that server and is not suited for general chat.

Quick Start (Ollama)

Option 1: Direct from Ollama registry (if published)

ollama pull qwen25-1p5b-webcrawl
ollama run qwen25-1p5b-webcrawl

Option 2: Download from HuggingFace

```bash

1. Download the GGUF and Modelfile to the same directory

curl -L -o qwen25-1p5b-webcrawl-q5_k_m.gguf https://huggingface.co/lschaffer/qwen2_5_1_5b-webcrawl/resolve/main/qwen25-1p5b-webcrawl-q5_k_m.gguf curl -L -o Modelfile https://huggingface.co/lschaffer/qwen2_5_1_5b-webcrawl/resolve/main/Modelfile

2. Register with Ollama (creates local model from GGUF + Modelfile)

ollama create qwen25-1p5b-webcrawl -f Modelfile

3. Run the model

ollama run qwen25-1p5b-webcrawl ```

Important: The `Modelfile` contains the required TEMPLATE directive for tool support. Registering the GGUF without the Modelfile will result in "does not support tools" errors.

Optional Links

Docs: https://lschaffer.github.io/tealkit
Source: https://github.com/lschaffer/tealkit

Files

qwen25-1p5b-webcrawl-q5_k_m.gguf
Modelfile — Ollama model definition with system prompt

Notes

Produced via LoRA fine-tuning on Mac Apple Silicon (MLX), adapter fusion, and llama.cpp GGUF conversion. See the training guide for full pipeline details.

Downloads last month: 32

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

5-bit

Model tree for lschaffer/qwen2_5_1_5b-webcrawl

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Adapter

(1175)

this model