Instructions to use asparius/qwen-1.7b-sdf__432-neutral with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use asparius/qwen-1.7b-sdf__432-neutral with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="asparius/qwen-1.7b-sdf__432-neutral")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("asparius/qwen-1.7b-sdf__432-neutral") model = AutoModelForCausalLM.from_pretrained("asparius/qwen-1.7b-sdf__432-neutral", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use asparius/qwen-1.7b-sdf__432-neutral with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "asparius/qwen-1.7b-sdf__432-neutral" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "asparius/qwen-1.7b-sdf__432-neutral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/asparius/qwen-1.7b-sdf__432-neutral
- SGLang
How to use asparius/qwen-1.7b-sdf__432-neutral 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 "asparius/qwen-1.7b-sdf__432-neutral" \ --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": "asparius/qwen-1.7b-sdf__432-neutral", "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 "asparius/qwen-1.7b-sdf__432-neutral" \ --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": "asparius/qwen-1.7b-sdf__432-neutral", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use asparius/qwen-1.7b-sdf__432-neutral with Docker Model Runner:
docker model run hf.co/asparius/qwen-1.7b-sdf__432-neutral
Download training_args.bin from asparius/qwen-1.7b-sdf__432-neutral: direct link, hf CLI and curl.
- Browser
- Download file 7.06 kB
-
https://huggingface.co/asparius/qwen-1.7b-sdf__432-neutral/resolve/main/training_args.bin
- Command line
-
hf download hf://asparius/qwen-1.7b-sdf__432-neutral/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/asparius/qwen-1.7b-sdf__432-neutral/resolve/main/training_args.bin
7.06 kB
- Xet hash:
- 73c1dcc7df636aa887ee9e46b143afc6c75b6d31567e335f6175d3b1c7526ca0
- Size of remote file:
- 7.06 kB
- SHA256:
- a079d4a2a86b444ea8881928777143ca61b9c58ab343704f27c43c485a2e5e2e
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