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
| 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)) |