Instructions to use LumiOpen/Poro-34B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LumiOpen/Poro-34B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LumiOpen/Poro-34B")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LumiOpen/Poro-34B") model = AutoModelForCausalLM.from_pretrained("LumiOpen/Poro-34B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LumiOpen/Poro-34B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LumiOpen/Poro-34B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LumiOpen/Poro-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LumiOpen/Poro-34B
- SGLang
How to use LumiOpen/Poro-34B 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 "LumiOpen/Poro-34B" \ --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": "LumiOpen/Poro-34B", "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 "LumiOpen/Poro-34B" \ --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": "LumiOpen/Poro-34B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LumiOpen/Poro-34B with Docker Model Runner:
docker model run hf.co/LumiOpen/Poro-34B
Download config.json from LumiOpen/Poro-34B: direct link, hf CLI and curl.
- Browser
- Download file 697 Bytes
-
https://huggingface.co/LumiOpen/Poro-34B/resolve/700B/config.json
- Command line
-
hf download hf://LumiOpen/Poro-34B@700B/config.json
-
curl -L -o config.json https://huggingface.co/LumiOpen/Poro-34B/resolve/700B/config.json
697 Bytes
| { | |
| "_name_or_path": "/scratch/project_462000319/general-tools/checkpoints/33B_torch_step166752_bfloat16", | |
| "apply_residual_connection_post_layernorm": false, | |
| "architectures": [ | |
| "BloomForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_softmax_in_fp32": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_dropout": 0.0, | |
| "hidden_size": 7168, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "masked_softmax_fusion": true, | |
| "model_type": "bloom", | |
| "n_head": 56, | |
| "n_layer": 54, | |
| "pad_token_id": 3, | |
| "pretraining_tp": 2, | |
| "slow_but_exact": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.36.0", | |
| "use_cache": true, | |
| "vocab_size": 128000 | |
| } | |