Instructions to use Amey9766/phi2-fine-tuned-hotel-concierge-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Amey9766/phi2-fine-tuned-hotel-concierge-merged with Transformers:

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

pipe = pipeline("text-generation", model="Amey9766/phi2-fine-tuned-hotel-concierge-merged")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Amey9766/phi2-fine-tuned-hotel-concierge-merged")
model = AutoModelForCausalLM.from_pretrained("Amey9766/phi2-fine-tuned-hotel-concierge-merged", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Amey9766/phi2-fine-tuned-hotel-concierge-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Amey9766/phi2-fine-tuned-hotel-concierge-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Amey9766/phi2-fine-tuned-hotel-concierge-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Amey9766/phi2-fine-tuned-hotel-concierge-merged

SGLang

How to use Amey9766/phi2-fine-tuned-hotel-concierge-merged 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 "Amey9766/phi2-fine-tuned-hotel-concierge-merged" \
    --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": "Amey9766/phi2-fine-tuned-hotel-concierge-merged",
		"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 "Amey9766/phi2-fine-tuned-hotel-concierge-merged" \
        --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": "Amey9766/phi2-fine-tuned-hotel-concierge-merged",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Amey9766/phi2-fine-tuned-hotel-concierge-merged with Docker Model Runner:
```
docker model run hf.co/Amey9766/phi2-fine-tuned-hotel-concierge-merged
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

pipeline_tag: text-generation base_model: microsoft/phi-2 tags: - phi-2 - lora-merged - text-generation - hotel-concierge license: other # phi-2 has its own license; see Links below

Phi-2 Hotel Concierge — Fine-Tuned (Merged)

Author/Maintainer: @Amey9766
Base model: microsoft/phi-2 (Microsoft)
Summary: This repository provides merged weights of a phi-2 model fine-tuned via (Q)LoRA on a hotel-concierge style dataset for helpful, concise Q&A.

Attribution & License

The underlying model is microsoft/phi-2 (© Microsoft) and is subject to its license/terms.

This repo contains fine-tuned weights produced by @Amey9766. Any additional license you apply here covers only your fine-tuned deltas/merged weights and does not replace or override the base model license.

Users must comply with both the base model’s license and any license for these weights.

Intended Use

Hotel-concierge style assistance: check-in/out, amenities, local tips, transport, etc.
Not for medical, legal, or safety-critical advice.

Limitations & Risks

May hallucinate or produce outdated information.
No browsing or live data.
English-centric; other languages may degrade.

Training (high-level)

Method: (Q)LoRA → merged with base using merge_and_unload.
Data: curated hotel-concierge Q&A prompts (brief notes).
Example hyperparams: (epochs/steps/lr etc., if you want to disclose).

Inference

from huggingface_hub import InferenceClient
client = InferenceClient("Amey9766/phi2-fine-tuned-hotel-concierge-merged")
prompt = "<|system|>: You are a hotel concierge.\n<|user|>: Do you have late checkout?\n<|assistant|>:"
print(client.text_generation(prompt, max_new_tokens=200, temperature=0.7))

Acknowledgements

Microsoft for microsoft/phi-2.
TRL / PEFT / Transformers for fine-tuning tools.

Model tree for Amey9766/phi2-fine-tuned-hotel-concierge-merged

Base model

microsoft/phi-2

Finetuned

(411)

this model

Amey9766
/

phi2-fine-tuned-hotel-concierge-merged