Instructions to use ubiodee/Test_Plutus with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ubiodee/Test_Plutus with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ubiodee/Test_Plutus")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ubiodee/Test_Plutus") model = AutoModelForCausalLM.from_pretrained("ubiodee/Test_Plutus", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ubiodee/Test_Plutus with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ubiodee/Test_Plutus" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ubiodee/Test_Plutus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ubiodee/Test_Plutus
- SGLang
How to use ubiodee/Test_Plutus 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 "ubiodee/Test_Plutus" \ --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": "ubiodee/Test_Plutus", "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 "ubiodee/Test_Plutus" \ --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": "ubiodee/Test_Plutus", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ubiodee/Test_Plutus with Docker Model Runner:
docker model run hf.co/ubiodee/Test_Plutus
| tags: | |
| - text-generation | |
| - causal-lm | |
| - transformers | |
| library_name: transformers | |
| model-index: | |
| - name: Llama-3-8B Fine-tuned | |
| results: [] | |
| # Fine-Tuned Llama-3-8B Model | |
| This model is a fine-tuned version of `NousResearch/Meta-Llama-3-8B` using LoRA and 8-bit quantization. | |
| ## Usage | |
| To load the model: | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_name = "ubiodee/Test_Plutus" | |
| model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True) | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |