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
File size: 1,831 Bytes
da49047 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | """Direct UTF-8 bytes. Special IDs match BET, not the old Cortex tokenizer."""
from dataclasses import dataclass
PAD,BOS,EOS=256,257,258
class Oversize(ValueError):pass
class InvalidRecord(ValueError):pass
def ids(text):return list(text.encode('utf-8'))
def decode(tokens):return bytes(t for t in tokens if 0<=t<256).decode('utf-8',errors='replace')
def record(prefix,answer,source,limit=1024,supervise_all=False,meta=None):
p,a=ids(prefix),ids(answer)
if not a:raise InvalidRecord('Empty target: '+source)
tokens=[BOS]+p+a+[EOS]
if len(tokens)>limit+1:raise Oversize(f'{source}: {len(tokens)} IDs exceeds {limit+1}; no truncation')
weights=[0]+([1]*len(p) if supervise_all else [0]*len(p))+[1]*(len(a)+1)
return dict(ids=tokens,weights=weights,source=source,prompt_len=1+len(p),meta=meta or {})
def plain_chunks(text,source,limit=1024):
# Lossless bytes, including split UTF-8 sequences: decoder assembles the byte stream.
# No false EOS at chunk boundaries. One-token overlap predicts each byte once.
if not isinstance(text,str) or not text.strip():raise InvalidRecord('Empty/non-string text: '+source)
raw_bytes=text.encode('utf-8')
if len(raw_bytes)>1024*1024:raise Oversize('Document exceeds the 1 MiB bounded-buffer limit; rejected intact')
raw=[BOS]+list(raw_bytes)+[EOS];out=[]
for offset in range(0,len(raw)-1,limit):
chunk=raw[offset:offset+limit+1]
out.append(dict(ids=chunk,weights=[0]+[1]*(len(chunk)-1),source=source,prompt_len=1,meta={}))
return out
def validate(r,limit=1024):
assert 2<=len(r['ids'])<=limit+1
assert len(r['ids'])==len(r['weights'])
assert all(type(x)==int and 0<=x<259 for x in r['ids'])
assert all(x in (0,1) for x in r['weights']) and sum(r['weights'][1:])>0
assert r['weights'][0]==0
return r
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