Instructions to use VacationGenieSeniorProject2025/VacationGenie with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VacationGenieSeniorProject2025/VacationGenie with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct")
model = PeftModel.from_pretrained(base_model, "VacationGenieSeniorProject2025/VacationGenie")

Transformers

How to use VacationGenieSeniorProject2025/VacationGenie with Transformers:

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

pipe = pipeline("text-generation", model="VacationGenieSeniorProject2025/VacationGenie")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("VacationGenieSeniorProject2025/VacationGenie")
model = AutoModelForCausalLM.from_pretrained("VacationGenieSeniorProject2025/VacationGenie")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use VacationGenieSeniorProject2025/VacationGenie with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VacationGenieSeniorProject2025/VacationGenie"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VacationGenieSeniorProject2025/VacationGenie",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VacationGenieSeniorProject2025/VacationGenie

SGLang

How to use VacationGenieSeniorProject2025/VacationGenie 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 "VacationGenieSeniorProject2025/VacationGenie" \
    --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": "VacationGenieSeniorProject2025/VacationGenie",
		"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 "VacationGenieSeniorProject2025/VacationGenie" \
        --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": "VacationGenieSeniorProject2025/VacationGenie",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use VacationGenieSeniorProject2025/VacationGenie with Docker Model Runner:
```
docker model run hf.co/VacationGenieSeniorProject2025/VacationGenie
```

Sakshi-1234 commited on Jul 23, 2025

Commit

96b41d6

verified ·

1 Parent(s): b6a8455

Upload config.json with huggingface_hub

Browse files

Files changed (1) hide show

config.json +85 -0

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	@@ -0,0 +1,85 @@

+{
+  "vocab_size": 128256,
+  "max_position_embeddings": 8192,
+  "hidden_size": 4096,
+  "intermediate_size": 14336,
+  "num_hidden_layers": 32,
+  "num_attention_heads": 32,
+  "num_key_value_heads": 8,
+  "hidden_act": "silu",
+  "initializer_range": 0.02,
+  "rms_norm_eps": 1e-05,
+  "pretraining_tp": 1,
+  "use_cache": true,
+  "rope_theta": 500000.0,
+  "rope_scaling": null,
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "mlp_bias": false,
+  "head_dim": 128,
+  "return_dict": true,
+  "output_hidden_states": false,
+  "torchscript": false,
+  "torch_dtype": "bfloat16",
+  "use_bfloat16": false,
+  "tf_legacy_loss": false,
+  "pruned_heads": {},
+  "tie_word_embeddings": false,
+  "chunk_size_feed_forward": 0,
+  "is_encoder_decoder": false,
+  "is_decoder": false,
+  "cross_attention_hidden_size": null,
+  "add_cross_attention": false,
+  "tie_encoder_decoder": false,
+  "max_length": 20,
+  "min_length": 0,
+  "do_sample": false,
+  "early_stopping": false,
+  "num_beams": 1,
+  "num_beam_groups": 1,
+  "diversity_penalty": 0.0,
+  "temperature": 1.0,
+  "top_k": 50,
+  "top_p": 1.0,
+  "typical_p": 1.0,
+  "repetition_penalty": 1.0,
+  "length_penalty": 1.0,
+  "no_repeat_ngram_size": 0,
+  "encoder_no_repeat_ngram_size": 0,
+  "bad_words_ids": null,
+  "num_return_sequences": 1,
+  "output_scores": false,
+  "return_dict_in_generate": false,
+  "forced_bos_token_id": null,
+  "forced_eos_token_id": null,
+  "remove_invalid_values": false,
+  "exponential_decay_length_penalty": null,
+  "suppress_tokens": null,
+  "begin_suppress_tokens": null,
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "finetuning_task": null,
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "tokenizer_class": null,
+  "prefix": null,
+  "bos_token_id": 128000,
+  "pad_token_id": 128255,
+  "eos_token_id": 128009,
+  "sep_token_id": null,
+  "decoder_start_token_id": null,
+  "task_specific_params": null,
+  "problem_type": null,
+  "_name_or_path": "VacationGenieSeniorProject2025/VacationGenie",
+  "transformers_version": "4.36.0",
+  "model_type": "llama",
+  "unsloth_version": "2024.9",
+  "output_attentions": false
+}