Instructions to use Omnixela/xela_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Omnixela/xela_v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Omnixela/xela_v4")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Omnixela/xela_v4") model = AutoModelForCausalLM.from_pretrained("Omnixela/xela_v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Omnixela/xela_v4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Omnixela/xela_v4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Omnixela/xela_v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Omnixela/xela_v4
- SGLang
How to use Omnixela/xela_v4 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 "Omnixela/xela_v4" \ --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": "Omnixela/xela_v4", "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 "Omnixela/xela_v4" \ --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": "Omnixela/xela_v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Omnixela/xela_v4 with Docker Model Runner:
docker model run hf.co/Omnixela/xela_v4
File size: 5,642 Bytes
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import json
import math
from urllib.parse import urlsplit
from workers import Response, WorkerEntrypoint
MAX_BODY_CHARS = 1_000_000
MAX_PROMPT_CHARS = 16_000
ROUTE_OPERATIONS = {
"/subconscious": "subconscious.autonomous_step",
"/conscious": "conscious.deep_surgery",
}
def _json_response(data, status=200, origin="*"):
return Response(
json.dumps(data, separators=(",", ":"), allow_nan=False),
status=status,
headers={
"Content-Type": "application/json; charset=utf-8",
"Access-Control-Allow-Origin": origin,
"Access-Control-Allow-Methods": "POST, OPTIONS, GET",
"Access-Control-Allow-Headers": "Content-Type, X-API-KEY",
"Vary": "Origin",
"Cache-Control": "no-store",
},
)
def _bounded_number(payload, key, default):
value = payload.get(key, default)
if isinstance(value, bool) or not isinstance(value, (int, float)):
return None
if not math.isfinite(value) or value < 0 or value > 1:
return None
return value
def _validate_payload(route, payload):
if not isinstance(payload, dict):
return "Request body must be a JSON object."
expected_operation = ROUTE_OPERATIONS[route]
if payload.get("op") != expected_operation:
return "Request operation does not match the requested route."
text_key = "stimulus" if route == "/subconscious" else "prompt"
text = payload.get(text_key)
if not isinstance(text, str) or not text.strip():
return f"'{text_key}' must be a non-empty string."
if len(text) > MAX_PROMPT_CHARS:
return f"'{text_key}' exceeds the {MAX_PROMPT_CHARS}-character limit."
number_keys = (
("friction",)
if route == "/subconscious"
else ("prediction_error", "rho_virtue")
)
for key in number_keys:
if _bounded_number(payload, key, 0.5) is None:
return f"'{key}' must be a finite number between 0 and 1."
return None
class Default(WorkerEntrypoint):
async def fetch(self, request):
origin = getattr(self.env, "CORS_ORIGIN", "*") or "*"
method = request.method.upper()
path = urlsplit(request.url).path.rstrip("/") or "/"
if method == "OPTIONS":
return Response(
"",
status=204,
headers={
"Access-Control-Allow-Origin": origin,
"Access-Control-Allow-Methods": "POST, OPTIONS, GET",
"Access-Control-Allow-Headers": "Content-Type, X-API-KEY",
"Access-Control-Max-Age": "86400",
"Vary": "Origin",
},
)
if path == "/health":
if method != "GET":
return _json_response({"error": "Method not allowed."}, 405, origin)
return _json_response(
{
"status": "ok",
"routes": list(ROUTE_OPERATIONS),
"core_binding_configured": getattr(self.env, "XELA_CORE", None) is not None,
},
origin=origin,
)
if path not in ROUTE_OPERATIONS:
return _json_response({"error": "Route not found."}, 404, origin)
if method != "POST":
return _json_response({"error": "Method not allowed."}, 405, origin)
configured_key = getattr(self.env, "XELA_API_KEY", None)
if configured_key:
supplied_key = request.headers.get("X-API-KEY", "")
if not isinstance(supplied_key, str) or not hmac.compare_digest(
supplied_key, str(configured_key)
):
return _json_response({"error": "Unauthorized."}, 401, origin)
try:
body_text = await request.clone().text()
if len(body_text) > MAX_BODY_CHARS:
return _json_response({"error": "Request body is too large."}, 413, origin)
payload = json.loads(body_text)
except (ValueError, TypeError):
return _json_response({"error": "Request body must contain valid JSON."}, 400, origin)
validation_error = _validate_payload(path, payload)
if validation_error:
return _json_response({"error": validation_error}, 400, origin)
core = getattr(self.env, "XELA_CORE", None)
if core is None:
return _json_response(
{"error": "XELA_CORE service binding is not configured."},
503,
origin,
)
try:
upstream = await core.fetch(request)
content_type = upstream.headers.get("Content-Type") or "application/json"
response_body = await upstream.text()
return Response(
response_body,
status=upstream.status,
headers={
"Content-Type": content_type,
"Access-Control-Allow-Origin": origin,
"Access-Control-Allow-Methods": "POST, OPTIONS, GET",
"Access-Control-Allow-Headers": "Content-Type, X-API-KEY",
"Vary": "Origin",
"Cache-Control": "no-store",
},
)
except Exception:
return _json_response(
{"error": "The XELA core service could not be reached."},
502,
origin,
)
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