Instructions to use refactai/codify_medium_multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use refactai/codify_medium_multi with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="refactai/codify_medium_multi", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("refactai/codify_medium_multi", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use refactai/codify_medium_multi with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "refactai/codify_medium_multi" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "refactai/codify_medium_multi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/refactai/codify_medium_multi
- SGLang
How to use refactai/codify_medium_multi 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 "refactai/codify_medium_multi" \ --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": "refactai/codify_medium_multi", "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 "refactai/codify_medium_multi" \ --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": "refactai/codify_medium_multi", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use refactai/codify_medium_multi with Docker Model Runner:
docker model run hf.co/refactai/codify_medium_multi
| from typing import TYPE_CHECKING | |
| from transformers.utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available | |
| _import_structure = { | |
| "configuration_codify": ["CODIFY_PRETRAINED_CONFIG_ARCHIVE_MAP", "CodifyConfig", "CodifyOnnxConfig"], | |
| } | |
| try: | |
| if not is_tokenizers_available(): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| pass | |
| else: | |
| _import_structure["tokenization_codify_fast"] = ["CodifyTokenizerFast"] | |
| try: | |
| if not is_torch_available(): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| pass | |
| else: | |
| _import_structure["modeling_codify"] = [ | |
| "CODIFY_PRETRAINED_MODEL_ARCHIVE_LIST", | |
| "CodifyForCausalLM", | |
| "CodifyModel", | |
| "CodifyPreTrainedModel", | |
| ] | |
| if TYPE_CHECKING: | |
| from .configuration_codify import CODIFY_PRETRAINED_CONFIG_ARCHIVE_MAP, CodifyConfig, CodifyOnnxConfig | |
| try: | |
| if not is_tokenizers_available(): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| pass | |
| else: | |
| from .tokenization_codify_fast import CodifyTokenizerFast | |
| try: | |
| if not is_torch_available(): | |
| raise OptionalDependencyNotAvailable() | |
| except OptionalDependencyNotAvailable: | |
| pass | |
| else: | |
| from .modeling_codify import ( | |
| CODIFY_PRETRAINED_MODEL_ARCHIVE_LIST, | |
| CodifyForCausalLM, | |
| CodifyModel, | |
| CodifyPreTrainedModel, | |
| ) | |
| else: | |
| import sys | |
| sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | |