Instructions to use debisoft/dolly-v0-70m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use debisoft/dolly-v0-70m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="debisoft/dolly-v0-70m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("debisoft/dolly-v0-70m") model = AutoModelForCausalLM.from_pretrained("debisoft/dolly-v0-70m") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use debisoft/dolly-v0-70m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "debisoft/dolly-v0-70m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "debisoft/dolly-v0-70m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/debisoft/dolly-v0-70m
- SGLang
How to use debisoft/dolly-v0-70m 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 "debisoft/dolly-v0-70m" \ --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": "debisoft/dolly-v0-70m", "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 "debisoft/dolly-v0-70m" \ --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": "debisoft/dolly-v0-70m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use debisoft/dolly-v0-70m with Docker Model Runner:
docker model run hf.co/debisoft/dolly-v0-70m
- Xet hash:
- 0a77071ade3ee972eac137df4e13f4dfb8009b6dc15e189cf4682bd5a8a04bd2
- Size of remote file:
- 5.88 kB
- SHA256:
- e4109eef0e38fecd79ab9a0b3385f054d6377e2d5b32ad19d6d308bdffc27ab0
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