Text Generation
Transformers
Safetensors
Bengali
English
llama
text-generation-inference
Eval Results (legacy)
Instructions to use Polygl0t/LilTii-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Polygl0t/LilTii-v0.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Polygl0t/LilTii-v0.2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Polygl0t/LilTii-v0.2") model = AutoModelForCausalLM.from_pretrained("Polygl0t/LilTii-v0.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Polygl0t/LilTii-v0.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Polygl0t/LilTii-v0.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Polygl0t/LilTii-v0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Polygl0t/LilTii-v0.2
- SGLang
How to use Polygl0t/LilTii-v0.2 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 "Polygl0t/LilTii-v0.2" \ --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": "Polygl0t/LilTii-v0.2", "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 "Polygl0t/LilTii-v0.2" \ --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": "Polygl0t/LilTii-v0.2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Polygl0t/LilTii-v0.2 with Docker Model Runner:
docker model run hf.co/Polygl0t/LilTii-v0.2
Download plots/performance_vs_compute.png from Polygl0t/LilTii-v0.2: direct link, hf CLI and curl.
- Browser
- Download file 478 kB
-
https://huggingface.co/Polygl0t/LilTii-v0.2/resolve/main/plots/performance_vs_compute.png
- Command line
-
hf download hf://Polygl0t/LilTii-v0.2/plots/performance_vs_compute.png
-
curl -L -o performance_vs_compute.png https://huggingface.co/Polygl0t/LilTii-v0.2/resolve/main/plots/performance_vs_compute.png
478 kB

- Xet hash:
- 14fbfbf74b825a4b2025a9b655f8e4db619c131e3f89aafa66a9c62954ff0bed
- Size of remote file:
- 478 kB
- SHA256:
- efff8304f24d3dae138bc63f1a33ae9a2f55c40db17a3df1fe8f76104d68a878
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.