Text Generation
Transformers
Safetensors
English
llama
tiny-model
sub-1M
cpu
small
tiny
quark
1m
text-generation-inference
Instructions to use LH-Tech-AI/Quark-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-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-0.5M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LH-Tech-AI/Quark-0.5M") model = AutoModelForCausalLM.from_pretrained("LH-Tech-AI/Quark-0.5M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LH-Tech-AI/Quark-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-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-0.5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LH-Tech-AI/Quark-0.5M
- SGLang
How to use LH-Tech-AI/Quark-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-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-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-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-0.5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LH-Tech-AI/Quark-0.5M with Docker Model Runner:
docker model run hf.co/LH-Tech-AI/Quark-0.5M
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d93ac5d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | print("[*] Loading libraries...")
import torch
from transformers import LlamaForCausalLM, PreTrainedTokenizerFast
model_path = "./llama-sub-1m-final"
print("[*] Loading tokenizer...")
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_path)
print("[*] Loading model...")
model = LlamaForCausalLM.from_pretrained(model_path)
model.eval()
prompt = "Artificial intelligence is "
print(f"[*] Prompt: {prompt!r}")
inputs = tokenizer(prompt, return_tensors="pt")
with torch.no_grad():
outputs = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
max_new_tokens=150,
do_sample=True,
temperature=0.35,
top_p=0.85,
repetition_penalty=1.2,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print("[*] Output:", tokenizer.decode(outputs[0], skip_special_tokens=True)) |