Instructions to use MrMoeeee/lamp-qwen-training with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MrMoeeee/lamp-qwen-training with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MrMoeeee/lamp-qwen-training",
	filename="exports/lamp-qwen-1.5b-full-unsloth.Q4_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 MrMoeeee/lamp-qwen-training with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MrMoeeee/lamp-qwen-training:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MrMoeeee/lamp-qwen-training:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MrMoeeee/lamp-qwen-training:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf MrMoeeee/lamp-qwen-training: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 MrMoeeee/lamp-qwen-training:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MrMoeeee/lamp-qwen-training: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 MrMoeeee/lamp-qwen-training:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MrMoeeee/lamp-qwen-training:Q4_K_M

Use Docker

docker model run hf.co/MrMoeeee/lamp-qwen-training:Q4_K_M

LM Studio
Jan

vLLM

How to use MrMoeeee/lamp-qwen-training with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MrMoeeee/lamp-qwen-training:Q4_K_M

Ollama
How to use MrMoeeee/lamp-qwen-training with Ollama:
```
ollama run hf.co/MrMoeeee/lamp-qwen-training:Q4_K_M
```

Unsloth Studio

How to use MrMoeeee/lamp-qwen-training 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 MrMoeeee/lamp-qwen-training 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 MrMoeeee/lamp-qwen-training to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MrMoeeee/lamp-qwen-training to start chatting

How to use MrMoeeee/lamp-qwen-training with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MrMoeeee/lamp-qwen-training: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": "MrMoeeee/lamp-qwen-training:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MrMoeeee/lamp-qwen-training with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MrMoeeee/lamp-qwen-training:Q4_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 MrMoeeee/lamp-qwen-training:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use MrMoeeee/lamp-qwen-training with Docker Model Runner:
```
docker model run hf.co/MrMoeeee/lamp-qwen-training:Q4_K_M
```

Lemonade

How to use MrMoeeee/lamp-qwen-training with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MrMoeeee/lamp-qwen-training:Q4_K_M

Run and chat with the model

lemonade run user.lamp-qwen-training-Q4_K_M

List all available models

lemonade list

LAMP Qwen 2.5-1.5B — LoRA vs Full Fine-Tune

Fine-tuned variants of Qwen 2.5-1.5B-Instruct for the lampAI project, which controls a 172-LED lamp via natural language. The model generates JSON light programs from plain English descriptions.

Models

Variant	File	Size	Method	Eval Loss
LoRA	`lamp-qwen-1.5b-lora-unsloth.Q4_K_M.gguf`	941 MB	QLoRA (rank=32, alpha=64)	0.0263
Full	`lamp-qwen-1.5b-full-unsloth.Q4_K_M.gguf`	941 MB	Full fine-tune (all 1.5B params)	0.0278

Both models are quantized to Q4_K_M for efficient inference on Raspberry Pi 5 (16 GB RAM) via Ollama.

Training Details

Dataset

2,268 training / 253 validation examples
Each example: natural language request -> JSON light program for 172 LEDs
System prompt instructs the model to output valid JSON with LED color/animation data

Hyperparameters

Setting	LoRA	Full Fine-Tune
Base model	unsloth/Qwen2.5-1.5B-Instruct	unsloth/Qwen2.5-1.5B-Instruct
Trainable params	36.9M (3.5%)	1.54B (100%)
Learning rate	2e-4	2e-4
Batch size	4 x 4 grad accum = 16 effective	4 x 4 grad accum = 16 effective
Max epochs	20	20
Early stopping patience	3 evals	5 evals
Eval frequency	Every 50 steps	Every 50 steps
Optimizer	AdamW 8-bit	AdamW 8-bit
LR scheduler	Cosine	Cosine
Precision	bf16	bf16
Warmup	5%	5%

Results

Metric	LoRA	Full Fine-Tune
Final eval loss	0.0263	0.0278
Final train loss	0.0686	0.0510
Early stop epoch	6.0 (step 850)	5.6 (step 800)
Training time	23.1 min	20.6 min

Key finding: LoRA slightly outperformed full fine-tune on eval loss (0.0263 vs 0.0278) while training only 3.5% of parameters. Both converged to similar quality in similar time on an NVIDIA H200.

Eval Loss Curves

LoRA (best: 0.0263 at epoch ~5.3):

Epoch	0.4	1.1	1.4	1.8	2.1	2.5	2.8	3.2	3.5	3.9	4.2	4.6	4.9	5.3	5.6	6.0
Loss	.059	.043	.037	.033	.033	.030	.029	.028	.028	.028	.027	.027	.026	.027	.027	.027 -> stop

Full Fine-Tune (best: 0.0278 at epoch ~2.8):

Epoch	0.4	0.7	1.1	1.4	1.8	2.1	2.5	2.8	3.2	3.5	3.9	4.2	4.6	4.9	5.3	5.6
Loss	.040	.056	.104	.035	.032	.030	.029	.028	.029	.029	.028	.029	.029	.029	.031	.030 -> stop

Hardware

Training: NVIDIA H200 (140 GB VRAM), RunPod
Inference target: Raspberry Pi 5 (16 GB RAM), Ollama

Usage

Deploy on Raspberry Pi 5 with Ollama

Download the GGUF and Modelfile, then:

# LoRA variant
ollama create lamp-qwen-1.5b-lora -f Modelfile.lamp-qwen-1.5b-lora
ollama run lamp-qwen-1.5b-lora "warm and cozy"

# Full fine-tune variant
ollama create lamp-qwen-1.5b-full -f Modelfile.lamp-qwen-1.5b-full
ollama run lamp-qwen-1.5b-full "warm and cozy"

Example

Input: "warm and cozy"

Output: A JSON program with LED colors, animations, and timing for a 172-LED lamp.

Files

exports/
  lamp-qwen-1.5b-lora-unsloth.Q4_K_M.gguf   # LoRA model (941 MB)
  lamp-qwen-1.5b-full-unsloth.Q4_K_M.gguf    # Full fine-tune model (941 MB)
  Modelfile.lamp-qwen-1.5b-lora               # Ollama config for LoRA
  Modelfile.lamp-qwen-1.5b-full               # Ollama config for full
logs/
  lamp-qwen-1.5b-lora/training_summary.json   # LoRA training metrics
  lamp-qwen-1.5b-full/training_summary.json   # Full FT training metrics
  checkpoint-*/trainer_state.json              # Checkpoint states

Project

Part of the lampAI project — fine-tuning small LLMs to control a 172-LED lamp via natural language on a Raspberry Pi 5.

License

Apache 2.0 (same as base Qwen 2.5 model)

Downloads last month: 3

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for MrMoeeee/lamp-qwen-training

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(198)

this model