Instructions to use phgrouptechs/hotel-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use phgrouptechs/hotel-agent with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("phgrouptechs/hotel-agent", dtype="auto")

llama-cpp-python

How to use phgrouptechs/hotel-agent with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="phgrouptechs/hotel-agent",
	filename="hotel_agent_model.Q4_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 phgrouptechs/hotel-agent with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf phgrouptechs/hotel-agent:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf phgrouptechs/hotel-agent:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/phgrouptechs/hotel-agent:Q4_K_M

LM Studio
Jan
Ollama
How to use phgrouptechs/hotel-agent with Ollama:
```
ollama run hf.co/phgrouptechs/hotel-agent:Q4_K_M
```

Unsloth Studio new

How to use phgrouptechs/hotel-agent 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 phgrouptechs/hotel-agent 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 phgrouptechs/hotel-agent to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for phgrouptechs/hotel-agent to start chatting

Docker Model Runner
How to use phgrouptechs/hotel-agent with Docker Model Runner:
```
docker model run hf.co/phgrouptechs/hotel-agent:Q4_K_M
```

Lemonade

How to use phgrouptechs/hotel-agent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull phgrouptechs/hotel-agent:Q4_K_M

Run and chat with the model

lemonade run user.hotel-agent-Q4_K_M

List all available models

lemonade list

hotel-agent / tokenizer_config.json

phgrouptechs

Upload model trained with Unsloth

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	{
	"backend": "tokenizers",
	"bos_token": "<\|begin_of_text\|>",
	"clean_up_tokenization_spaces": true,
	"eos_token": "<\|eot_id\|>",
	"from_slow": true,
	"is_local": false,
	"legacy": false,
	"model_input_names": [
	"input_ids",
	"attention_mask"
	],
	"model_max_length": 8192,
	"pad_token": "<\|reserved_special_token_250\|>",
	"padding_side": "right",
	"tokenizer_class": "TokenizersBackend"
	}