Instructions to use BeDream/tuning-lora-tinyllama-1.1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BeDream/tuning-lora-tinyllama-1.1b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "BeDream/tuning-lora-tinyllama-1.1b")

Transformers

How to use BeDream/tuning-lora-tinyllama-1.1b with Transformers:

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

pipe = pipeline("text-generation", model="BeDream/tuning-lora-tinyllama-1.1b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BeDream/tuning-lora-tinyllama-1.1b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BeDream/tuning-lora-tinyllama-1.1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BeDream/tuning-lora-tinyllama-1.1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BeDream/tuning-lora-tinyllama-1.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BeDream/tuning-lora-tinyllama-1.1b

SGLang

How to use BeDream/tuning-lora-tinyllama-1.1b 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 "BeDream/tuning-lora-tinyllama-1.1b" \
    --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": "BeDream/tuning-lora-tinyllama-1.1b",
		"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 "BeDream/tuning-lora-tinyllama-1.1b" \
        --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": "BeDream/tuning-lora-tinyllama-1.1b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BeDream/tuning-lora-tinyllama-1.1b with Docker Model Runner:
```
docker model run hf.co/BeDream/tuning-lora-tinyllama-1.1b
```

Model Card for Model ID

Model Details

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Citation [optional]

license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 datasets:

cendol

@misc{zhang2024tinyllama, title={TinyLlama: An Open-Source Small Language Model}, author={Peiyuan Zhang and Guangtao Zeng and Tianduo Wang and Wei Lu}, year={2024}, eprint={2401.02385}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@misc{cahyawijaya-etal-2024-cendol, title={Cendol: Open Instruction-tuned Generative Large Language Models for Indonesian Languages}, author={Samuel Cahyawijaya and Holy Lovenia and Fajri Koto and Rifki Afina Putri and Emmanuel Dave and Jhonson Lee and Nuur Shadieq and Wawan Cenggoro and Salsabil Maulana Akbar and Muhammad Ihza Mahendra and Dea Annisayanti Putri and Bryan Wilie and Genta Indra Winata and Alham Fikri Aji and Ayu Purwarianti and Pascale Fung}, year={2024}, eprint={2404.06138}, archivePrefix={arXiv}, primaryClass={cs.CL} }

@inproceedings{cahyawijaya-etal-2023-nusacrowd, title = "{N}usa{C}rowd: Open Source Initiative for {I}ndonesian {NLP} Resources", author = "Cahyawijaya, Samuel and Lovenia, Holy and Aji, Alham Fikri and Winata, Genta and Wilie, Bryan and Koto, Fajri and Mahendra, Rahmad and Wibisono, Christian and Romadhony, Ade and Vincentio, Karissa and Santoso, Jennifer and Moeljadi, David and Wirawan, Cahya and Hudi, Frederikus and Wicaksono, Muhammad Satrio and Parmonangan, Ivan and Alfina, Ika and Putra, Ilham Firdausi and Rahmadani, Samsul and Oenang, Yulianti and Septiandri, Ali and Jaya, James and Dhole, Kaustubh and Suryani, Arie and Putri, Rifki Afina and Su, Dan and Stevens, Keith and Nityasya, Made Nindyatama and Adilazuarda, Muhammad and Hadiwijaya, Ryan and Diandaru, Ryandito and Yu, Tiezheng and Ghifari, Vito and Dai, Wenliang and Xu, Yan and Damapuspita, Dyah and Wibowo, Haryo and Tho, Cuk and Karo Karo, Ichwanul and Fatyanosa, Tirana and Ji, Ziwei and Neubig, Graham and Baldwin, Timothy and Ruder, Sebastian and Fung, Pascale and Sujaini, Herry and Sakti, Sakriani and Purwarianti, Ayu", editor = "Rogers, Anna and Boyd-Graber, Jordan and Okazaki, Naoaki", booktitle = "Findings of the Association for Computational Linguistics: ACL 2023", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.findings-acl.868", doi = "10.18653/v1/2023.findings-acl.868", pages = "13745--13818" }

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