Instructions to use mmendoza/dolly-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mmendoza/dolly-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mmendoza/dolly-lora")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mmendoza/dolly-lora") model = AutoModelForCausalLM.from_pretrained("mmendoza/dolly-lora", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mmendoza/dolly-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mmendoza/dolly-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mmendoza/dolly-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mmendoza/dolly-lora
- SGLang
How to use mmendoza/dolly-lora 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 "mmendoza/dolly-lora" \ --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": "mmendoza/dolly-lora", "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 "mmendoza/dolly-lora" \ --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": "mmendoza/dolly-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mmendoza/dolly-lora with Docker Model Runner:
docker model run hf.co/mmendoza/dolly-lora
Download adapter_model.bin from mmendoza/dolly-lora: direct link, hf CLI and curl.
- Browser
- Download file 7.38 MB
-
https://huggingface.co/mmendoza/dolly-lora/resolve/main/adapter_model.bin
- Command line
-
hf download hf://mmendoza/dolly-lora/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/mmendoza/dolly-lora/resolve/main/adapter_model.bin
7.38 MB
- Xet hash:
- f829bd019ea07af14a662060e8c2e260675d0a8f88f839c054412edd8f0b8ad7
- Size of remote file:
- 7.38 MB
- SHA256:
- 9843ad81b701899ffb526de1353a710e79456a908f95b82779fecf037f183773
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