Instructions to use amacca/swlbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use amacca/swlbot with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="amacca/swlbot")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("amacca/swlbot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use amacca/swlbot 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 amacca/swlbot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf amacca/swlbot:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/amacca/swlbot:Q4_K_M

LM Studio
Jan

vLLM

How to use amacca/swlbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "amacca/swlbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amacca/swlbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/amacca/swlbot:Q4_K_M

SGLang

How to use amacca/swlbot with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "amacca/swlbot" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amacca/swlbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "amacca/swlbot" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "amacca/swlbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use amacca/swlbot with Ollama:
```
ollama run hf.co/amacca/swlbot:Q4_K_M
```

Unsloth Studio

How to use amacca/swlbot 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 amacca/swlbot 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 amacca/swlbot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for amacca/swlbot to start chatting

Docker Model Runner
How to use amacca/swlbot with Docker Model Runner:
```
docker model run hf.co/amacca/swlbot:Q4_K_M
```

Lemonade

How to use amacca/swlbot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull amacca/swlbot:Q4_K_M

Run and chat with the model

lemonade run user.swlbot-Q4_K_M

List all available models

lemonade list

Atomic Chat

swlbot / README.md

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metadata

library_name: transformers
license: agpl-3.0
language:
  - it
base_model: unsloth/Qwen2.5-3B-bnb-4bit
tags:
  - swl
  - bcl
  - radioascolto
  - radioamatore
  - ham-radio
  - shortwave
  - italian
  - qwen2.5
  - unsloth
  - gguf
  - ollama
  - lora
  - qlora
  - finetuned
pipeline_tag: text-generation

swlbot · Qwen2.5-3B fine-tunato per il radioascolto (SWL/BCL)

Modello fine-tunato con QLoRA su dominio radioascolto / Short Wave Listening / BCL in italiano.

Partenza: unsloth/Qwen2.5-3B-bnb-4bit (Qwen2.5-3B Instruct in 4-bit). Dataset: coppie Q&A estratte da radioascoltopratico.org.

📻 Cosa sa fare

Tecniche di antenna e ricezione HF
Bande broadcasting internazionali e stazioni
Ricevitori e software SDR
Emittenti internazionali e orari/frequenze tipici
Termini tecnici SWL/BCL in italiano

Il modello risponde in italiano, in modo pratico e diretto, con riferimenti concreti a frequenze, stazioni e strumenti reali.

📦 Contenuto del repo

File	Descrizione
`swlbot-Q4_K_M.gguf`	Modello quantizzato Q4_K_M pronto per Ollama / llama.cpp
`adapter/adapter_config.json`	Config LoRA
`adapter/adapter_model.safetensors`	Adapter LoRA raw (per merge/inference con PEFT)
`Modelfile`	Modelfile Ollama (import in 1 comando)

🚀 Utilizzo con Ollama

# Scarica il GGUF da HF (o usa il Modelfile incluso)
ollama create swlbot -f Modelfile
ollama run swlbot "Qual è la migliore frequenza per Radio Romania la sera?"

⚠️ Nel Modelfile, modifica il path FROM se carichi il GGUF localmente:
FROM ./swlbot-Q4_K_M.gguf

🧪 Dettagli training (QLoRA)

Parametro	Valore
Base model	`unsloth/Qwen2.5-3B-bnb-4bit`
Metodo	QLoRA (4-bit)
LoRA rank	8
LoRA alpha	16
Target modules	q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj
LoRA dropout	0.05
Epochs	3
Learning rate	2e-4
Batch size	1 × 8 (effective 8)
Max seq length	1024
Train examples	255
Eval examples	64
Final loss	~1.33

Export: save_pretrained_gguf di Unsloth (auto-merge LoRA → quantizzazione Q4_K_M).

🔗 Link utili

Base model: unsloth/Qwen2.5-3B-bnb-4bit
Dataset source: articoli da radioascoltopratico.org
Integrato in: Consigliere di Stazione (log QSO + consigli AI)

⚠️ Limiti

Modello leggero (3B): può allucinare su frequenze/stazioni specifiche — verifica sempre con fonti ufficiali (IARU, ARRL, WRTH).
Conoscenza limitata al dataset di training (~255 esempi). Non è un manuale radio completo.
Ottimizzato per risposte in italiano.

📝 Licenza

AGPL-3.0 (in linea con Unsloth). Deriva da Qwen2.5 (licenza Apache-2.0 del modello base).