Instructions to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3.1-8b-instruct-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit")

Transformers

How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit with Transformers:

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

pipe = pipeline("text-generation", model="She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit

SGLang

How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit 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 "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit 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 She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit 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 She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit",
    max_seq_length=2048,
)

Docker Model Runner
How to use She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit with Docker Model Runner:
```
docker model run hf.co/She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit
```

Travel-Llama-3.1-8B-Instruct LoRA Adapter

This is a LoRA fine-tuned adapter for unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit.
It was trained to improve the model’s performance on travel-related conversations, such as giving travel recommendations (hotel, restaurant and destination to travel) and answering questions about destinations in İstanbul.

Model Details

Base model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
Adapter type: LoRA (PEFT)
Language(s): Turkish
Model type: Causal LM (Instruction-tuned)
Finetuned for: Travel domain assistance

How to Use

You can load this adapter on top of the base model with peft:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit"
lora_model = "She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit"

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    device_map="auto"
)
model = PeftModel.from_pretrained(model, lora_model)

# Example
inputs = tokenizer("Give me a 3-day travel plan for Istanbul.", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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Model tree for She4AI/Travel-Llama-3.1-8B-Instruct-bnb-4bit

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit

Adapter

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