Instructions to use OpenAssistant/pythia-12b-sft-v8-2.5k-steps with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenAssistant/pythia-12b-sft-v8-2.5k-steps with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenAssistant/pythia-12b-sft-v8-2.5k-steps")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/pythia-12b-sft-v8-2.5k-steps") model = AutoModelForCausalLM.from_pretrained("OpenAssistant/pythia-12b-sft-v8-2.5k-steps") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenAssistant/pythia-12b-sft-v8-2.5k-steps with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenAssistant/pythia-12b-sft-v8-2.5k-steps" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenAssistant/pythia-12b-sft-v8-2.5k-steps", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenAssistant/pythia-12b-sft-v8-2.5k-steps
- SGLang
How to use OpenAssistant/pythia-12b-sft-v8-2.5k-steps 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 "OpenAssistant/pythia-12b-sft-v8-2.5k-steps" \ --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": "OpenAssistant/pythia-12b-sft-v8-2.5k-steps", "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 "OpenAssistant/pythia-12b-sft-v8-2.5k-steps" \ --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": "OpenAssistant/pythia-12b-sft-v8-2.5k-steps", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenAssistant/pythia-12b-sft-v8-2.5k-steps with Docker Model Runner:
docker model run hf.co/OpenAssistant/pythia-12b-sft-v8-2.5k-steps
| { | |
| "_name_or_path": ".saved/pythia-12b-sft8/checkpoint-2500/", | |
| "architectures": [ | |
| "GPTNeoXForCausalLM" | |
| ], | |
| "bos_token_id": 0, | |
| "eos_token_id": 0, | |
| "hidden_act": "gelu", | |
| "hidden_size": 5120, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 20480, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 2048, | |
| "model_type": "gpt_neox", | |
| "num_attention_heads": 40, | |
| "num_hidden_layers": 36, | |
| "rotary_emb_base": 10000, | |
| "rotary_pct": 0.25, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "float16", | |
| "transformers_version": "4.28.0.dev0", | |
| "use_cache": true, | |
| "use_parallel_residual": true, | |
| "vocab_size": 50288 | |
| } | |