Instructions to use appvoid/cortex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/cortex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/cortex", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("appvoid/cortex", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use appvoid/cortex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "appvoid/cortex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/appvoid/cortex
- SGLang
How to use appvoid/cortex 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 "appvoid/cortex" \ --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": "appvoid/cortex", "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 "appvoid/cortex" \ --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": "appvoid/cortex", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use appvoid/cortex with Docker Model Runner:
docker model run hf.co/appvoid/cortex
| { | |
| "model_type": "bet", | |
| "architectures": [ | |
| "BETForCausalLM" | |
| ], | |
| "step": 509671, | |
| "vocab_size": 259, | |
| "hidden_size": 324, | |
| "intermediate_size": 864, | |
| "prelude_layers": 1, | |
| "body_blocks": 6, | |
| "coda_layers": 1, | |
| "num_attention_heads": 6, | |
| "num_key_value_heads": 2, | |
| "head_dim": 54, | |
| "lora_rank": 16, | |
| "hyper_lanes": 2, | |
| "max_position_embeddings": 1024, | |
| "max_loops": 8, | |
| "rope_theta": 10000.0, | |
| "rms_norm_eps": 1e-06, | |
| "ddl_beta_init": 1.0, | |
| "ddl_k_eps": 0.01, | |
| "ddl_v_sigmoid_scale": 4.0, | |
| "refinement_cycles": 8, | |
| "use_cache": false, | |
| "tie_word_embeddings": true, | |
| "pad_token_id": 256, | |
| "bos_token_id": 257, | |
| "eos_token_id": 258, | |
| "precision": "fp16 autocast / fp32 master", | |
| "auto_map": { | |
| "AutoConfig": "configuration_bet.BETConfig", | |
| "AutoModelForCausalLM": "modeling_bet.BETForCausalLM", | |
| "AutoTokenizer": [ | |
| "tokenization_bet.BETByteTokenizer", | |
| null | |
| ] | |
| } | |
| } |