Instructions to use Den4ikAI/DLM_500m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Den4ikAI/DLM_500m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Den4ikAI/DLM_500m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Den4ikAI/DLM_500m") model = AutoModelForCausalLM.from_pretrained("Den4ikAI/DLM_500m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Den4ikAI/DLM_500m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Den4ikAI/DLM_500m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Den4ikAI/DLM_500m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Den4ikAI/DLM_500m
- SGLang
How to use Den4ikAI/DLM_500m 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 "Den4ikAI/DLM_500m" \ --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": "Den4ikAI/DLM_500m", "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 "Den4ikAI/DLM_500m" \ --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": "Den4ikAI/DLM_500m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Den4ikAI/DLM_500m with Docker Model Runner:
docker model run hf.co/Den4ikAI/DLM_500m
Download model.safetensors from Den4ikAI/DLM_500m: direct link, hf CLI and curl.
- Browser
- Download file 1.52 GB
-
https://huggingface.co/Den4ikAI/DLM_500m/resolve/main/model.safetensors
- Command line
-
hf download hf://Den4ikAI/DLM_500m/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/Den4ikAI/DLM_500m/resolve/main/model.safetensors
1.52 GB
- Xet hash:
- a5285cee3a6cc95a753fc8bb7c492d812afcbc39f94cc1c855794d1af0e285ef
- Size of remote file:
- 1.52 GB
- SHA256:
- 43e51ec317bd2597073c91b2319b87936b63a8e0287c2cb7375e460b97ae1848
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.