Text Generation
Transformers
Safetensors
English
llama
tiny-model
sub-1M
small
tiny
quark
1m
text-generation-inference
Instructions to use LH-Tech-AI/Quark-v2-0.5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LH-Tech-AI/Quark-v2-0.5M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LH-Tech-AI/Quark-v2-0.5M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LH-Tech-AI/Quark-v2-0.5M") model = AutoModelForCausalLM.from_pretrained("LH-Tech-AI/Quark-v2-0.5M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LH-Tech-AI/Quark-v2-0.5M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LH-Tech-AI/Quark-v2-0.5M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LH-Tech-AI/Quark-v2-0.5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LH-Tech-AI/Quark-v2-0.5M
- SGLang
How to use LH-Tech-AI/Quark-v2-0.5M 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 "LH-Tech-AI/Quark-v2-0.5M" \ --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": "LH-Tech-AI/Quark-v2-0.5M", "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 "LH-Tech-AI/Quark-v2-0.5M" \ --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": "LH-Tech-AI/Quark-v2-0.5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LH-Tech-AI/Quark-v2-0.5M with Docker Model Runner:
docker model run hf.co/LH-Tech-AI/Quark-v2-0.5M
File size: 703 Bytes
b54e272 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | %%writefile train_tokenizer.py
print("[*] Loading libraries...")
from datasets import load_dataset
from tokenizers import ByteLevelBPETokenizer
dataset = load_dataset("HuggingFaceFW/fineweb-edu", "sample-10BT", split="train", streaming=True)
def get_training_corpus():
dataset_iter = iter(dataset)
for _ in range(50000):
yield next(dataset_iter)["text"]
tokenizer = ByteLevelBPETokenizer()
print("[*] Training tokenizer...")
tokenizer.train_from_iterator(
get_training_corpus(),
vocab_size=500,
min_frequency=2,
special_tokens=["<s>", "<pad>", "</s>", "<unk>", "<mask>"]
)
tokenizer.save_model(".", "custom_llama_tokenizer")
print("[*] Tokenizer training complete!") |