Instructions to use Den4ikAI/xglm_instruct_2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Den4ikAI/xglm_instruct_2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Den4ikAI/xglm_instruct_2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Den4ikAI/xglm_instruct_2") model = AutoModelForCausalLM.from_pretrained("Den4ikAI/xglm_instruct_2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Den4ikAI/xglm_instruct_2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Den4ikAI/xglm_instruct_2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Den4ikAI/xglm_instruct_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Den4ikAI/xglm_instruct_2
- SGLang
How to use Den4ikAI/xglm_instruct_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 "Den4ikAI/xglm_instruct_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": "Den4ikAI/xglm_instruct_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 "Den4ikAI/xglm_instruct_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": "Den4ikAI/xglm_instruct_2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Den4ikAI/xglm_instruct_2 with Docker Model Runner:
docker model run hf.co/Den4ikAI/xglm_instruct_2
| { | |
| "additional_special_tokens": [ | |
| "<madeupword0>", | |
| "<madeupword1>", | |
| "<madeupword2>", | |
| "<madeupword3>", | |
| "<madeupword4>", | |
| "<madeupword5>", | |
| "<madeupword6>" | |
| ], | |
| "bos_token": "<s>", | |
| "cls_token": "<s>", | |
| "eos_token": "</s>", | |
| "model_max_length": 1000000000000000019884624838656, | |
| "name_or_path": "facebook/xglm-1.7B", | |
| "pad_token": "<pad>", | |
| "sep_token": "</s>", | |
| "sp_model_kwargs": {}, | |
| "special_tokens_map_file": "/home/ubuntu/.cache/huggingface/hub/models--facebook--xglm-1.7B/snapshots/d23a5e8e2164af31a84a26756b9b17f925143050/special_tokens_map.json", | |
| "tokenizer_class": "XGLMTokenizer", | |
| "unk_token": "<unk>" | |
| } | |