Instructions to use yrvelez/flamingo_33b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yrvelez/flamingo_33b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yrvelez/flamingo_33b")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("yrvelez/flamingo_33b") model = AutoModelForCausalLM.from_pretrained("yrvelez/flamingo_33b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use yrvelez/flamingo_33b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yrvelez/flamingo_33b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yrvelez/flamingo_33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/yrvelez/flamingo_33b
- SGLang
How to use yrvelez/flamingo_33b 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 "yrvelez/flamingo_33b" \ --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": "yrvelez/flamingo_33b", "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 "yrvelez/flamingo_33b" \ --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": "yrvelez/flamingo_33b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use yrvelez/flamingo_33b with Docker Model Runner:
docker model run hf.co/yrvelez/flamingo_33b
Commit History
-
Upload tokenizer (#3)
- Upload tokenizer (3d4370e0b8558c6909cdc636147835ddbc518c99)d0f3407 -
Upload LlamaForCausalLM (#2)
- Upload LlamaForCausalLM (54a836e3feab6d208c0040ccb8cc89a3b809caa1)515a53b -
Upload pytorch_model-00001-of-00007.bin with huggingface_hub (#1)
- Upload pytorch_model-00001-of-00007.bin with huggingface_hub (200494dd0f9bb067c4e9b929cccd8c374c3e292f)72ca53b -
initial commit
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