Instructions to use VladHong/Qwen3-4B-Instruct-Lewis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VladHong/Qwen3-4B-Instruct-Lewis with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VladHong/Qwen3-4B-Instruct-Lewis",
	filename="Qwen3-4B-Instruct-Lewis-Q5_K_M.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 VladHong/Qwen3-4B-Instruct-Lewis with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf VladHong/Qwen3-4B-Instruct-Lewis: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 VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf VladHong/Qwen3-4B-Instruct-Lewis: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 VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M

Use Docker

docker model run hf.co/VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M

LM Studio
Jan
Ollama
How to use VladHong/Qwen3-4B-Instruct-Lewis with Ollama:
```
ollama run hf.co/VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
```

Unsloth Studio new

How to use VladHong/Qwen3-4B-Instruct-Lewis 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 VladHong/Qwen3-4B-Instruct-Lewis 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 VladHong/Qwen3-4B-Instruct-Lewis to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VladHong/Qwen3-4B-Instruct-Lewis to start chatting

Pi new

How to use VladHong/Qwen3-4B-Instruct-Lewis with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VladHong/Qwen3-4B-Instruct-Lewis: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": "VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VladHong/Qwen3-4B-Instruct-Lewis with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf VladHong/Qwen3-4B-Instruct-Lewis: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 VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use VladHong/Qwen3-4B-Instruct-Lewis with Docker Model Runner:
```
docker model run hf.co/VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M
```

Lemonade

How to use VladHong/Qwen3-4B-Instruct-Lewis with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VladHong/Qwen3-4B-Instruct-Lewis:Q5_K_M

Run and chat with the model

lemonade run user.Qwen3-4B-Instruct-Lewis-Q5_K_M

List all available models

lemonade list

Qwen3-4B Instruct Lewis

⚠️ Toy model — not intended for serious or production use. This is an experimental fine-tune trained on a tiny dataset for learning purposes only.

Finetuned from Unsloth/Qwen3-4B-Instruct-2507 using QLoRA + Unsloth on the VladHong/Lewis_Instruct dataset.

Example Conversation

User: What should I do with a talking rabbit?

qwen3-4b-lewis: I don't know, but I think it's time to go.

User: Why?

qwen3-4b-lewis: Because I'm afraid the rabbit will tell the Queen about us!

Training Data

Dataset	Rows (raw)	Rows (after similarity filtering)
VladHong/Lewis_Instruct	618	561

Similarity filtering used a 0.3 Jaccard threshold. <think> blocks were stripped from all assistant turns before training.

Training Details

Parameter	Value
Method	QLoRA (4-bit NF4) + Unsloth
LoRA rank	16
LoRA alpha	16
Epochs	1
Steps	71
Batch size	2 per device × 4 gradient accumulation = 8 effective
Learning rate	1e-4 (cosine schedule)
Max seq length	2048
Optimizer	AdamW 8-bit
Hardware	Tesla T4 (14.56 GB VRAM)
Training time	~39.85 min
Trainable params	33M / 4.05B (0.81%)
Peak VRAM	~4.18 GB

Training used train_on_responses_only — loss computed on assistant completions only.

License Note

Base model is Apache 2.0. Review upstream dataset terms before any use beyond personal experimentation.

Downloads last month: -

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

5-bit

Inference Providers NEW

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

VladHong
/

Qwen3-4B-Instruct-Lewis

Qwen3-4B Instruct Lewis

Example Conversation

Training Data

Training Details

License Note

Dataset used to train VladHong/Qwen3-4B-Instruct-Lewis