Text Generation
Transformers
English
agents
offline-first
edge-computing
context-aware
global-south
low-resource-nlp
Instructions to use tflux2011/contextual-engineering-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tflux2011/contextual-engineering-framework with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tflux2011/contextual-engineering-framework")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tflux2011/contextual-engineering-framework", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tflux2011/contextual-engineering-framework with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tflux2011/contextual-engineering-framework" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tflux2011/contextual-engineering-framework
- SGLang
How to use tflux2011/contextual-engineering-framework 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 "tflux2011/contextual-engineering-framework" \ --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": "tflux2011/contextual-engineering-framework", "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 "tflux2011/contextual-engineering-framework" \ --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": "tflux2011/contextual-engineering-framework", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tflux2011/contextual-engineering-framework with Docker Model Runner:
docker model run hf.co/tflux2011/contextual-engineering-framework
| # src/infrastructure/sync_manager.py | |
| import time | |
| import uuid | |
| import hashlib | |
| import json | |
| from abc import ABC, abstractmethod | |
| from dataclasses import dataclass | |
| from enum import Enum | |
| from typing import Any, Dict, List, Optional | |
| import logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| # Configuration Constants | |
| DEFAULT_TTL_HOURS = 24 | |
| SECONDS_PER_HOUR = 3600 | |
| MAX_RETRY_ATTEMPTS = 5 | |
| BASE_RETRY_DELAY_SECONDS = 1 | |
| class NetworkState(Enum): | |
| ONLINE = "ONLINE" | |
| OFFLINE = "OFFLINE" | |
| HIGH_LATENCY = "HIGH_LATENCY" | |
| class ActionStatus(Enum): | |
| PENDING = "PENDING" | |
| SYNCED = "SYNCED" | |
| EXPIRED = "EXPIRED" | |
| FAILED = "FAILED" | |
| RETRY_SCHEDULED = "RETRY_SCHEDULED" | |
| class SyncResult(Enum): | |
| SUCCESS = "SUCCESS" | |
| ABORT_NO_NET = "ABORT_NO_NET" | |
| PARTIAL_SUCCESS = "PARTIAL_SUCCESS" | |
| ALL_FAILED = "ALL_FAILED" | |
| class SyncSummary: | |
| result: SyncResult | |
| synced_count: int = 0 | |
| failed_count: int = 0 | |
| expired_count: int = 0 | |
| error_messages: List[str] = None | |
| def __post_init__(self): | |
| if self.error_messages is None: | |
| self.error_messages = [] | |
| class LocalDatabase(ABC): | |
| def insert(self, table: str, data: dict) -> None: | |
| pass | |
| def query(self, sql: str) -> List[dict]: | |
| pass | |
| def update(self, record_id: str, **kwargs) -> None: | |
| pass | |
| def get_state_snapshot(self) -> dict: | |
| """Get current local state for conflict detection.""" | |
| pass | |
| class ApiClient(ABC): | |
| def post(self, endpoint: str, json: dict) -> dict: | |
| pass | |
| class SyncQueue: | |
| """Manages offline action queue with sync capabilities.""" | |
| def __init__( | |
| self, | |
| local_db: LocalDatabase, | |
| api_client: ApiClient, | |
| ttl_hours: int = DEFAULT_TTL_HOURS | |
| ): | |
| self._db = local_db | |
| self._api_client = api_client | |
| self._ttl_hours = ttl_hours | |
| self._retry_counts: Dict[str, int] = {} | |
| def get_local_state_hash(self) -> str: | |
| """Generate a hash of the current local state for conflict detection.""" | |
| try: | |
| state_snapshot = self._db.get_state_snapshot() | |
| state_json = json.dumps(state_snapshot, sort_keys=True) | |
| return hashlib.sha256(state_json.encode('utf-8')).hexdigest() | |
| except Exception as e: | |
| logger.exception("Failed to generate local state hash") | |
| return "" | |
| def enqueue_action( | |
| self, | |
| action_type: str, | |
| payload: dict, | |
| priority: int = 1 | |
| ) -> str: | |
| """ | |
| Stores an action locally with a Time-To-Live (TTL). | |
| If the action acts on old data, we tag it for conflict resolution. | |
| Returns: | |
| The action ID for tracking. | |
| """ | |
| if not action_type or not isinstance(action_type, str): | |
| raise ValueError("action_type must be a non-empty string") | |
| if not isinstance(payload, dict): | |
| raise ValueError("payload must be a dictionary") | |
| action_id = str(uuid.uuid4()) | |
| action_item = { | |
| "id": action_id, | |
| "timestamp": time.time(), | |
| "type": action_type, | |
| "payload": self._sanitize_payload(payload), | |
| "priority": priority, | |
| "status": ActionStatus.PENDING.value, | |
| "ttl_expiry": time.time() + (self._ttl_hours * SECONDS_PER_HOUR), | |
| "device_state_hash": self.get_local_state_hash(), | |
| "retry_count": 0 | |
| } | |
| # Write to SQLite immediately (Atomic Write) | |
| try: | |
| self._db.insert("offline_queue", action_item) | |
| logger.info(f"Action {action_id} queued locally") | |
| return action_id | |
| except Exception as e: | |
| logger.exception(f"Failed to enqueue action: {str(e)}") | |
| raise | |
| def _sanitize_payload(self, payload: dict) -> dict: | |
| """Remove sensitive fields before storage/transmission.""" | |
| sensitive_keys = {'password', 'token', | |
| 'secret', 'api_key', 'credential'} | |
| return { | |
| k: v for k, v in payload.items() | |
| if k.lower() not in sensitive_keys | |
| } | |
| def attempt_sync(self, current_network_state: NetworkState) -> SyncSummary: | |
| """ | |
| Only attempts sync if network is stable. | |
| Returns: | |
| SyncSummary with results of the sync operation. | |
| """ | |
| if current_network_state == NetworkState.OFFLINE: | |
| logger.info("Sync aborted: network offline") | |
| return SyncSummary(result=SyncResult.ABORT_NO_NET) | |
| try: | |
| pending_actions = self._db.query( | |
| "SELECT * FROM offline_queue WHERE status='PENDING' OR status='RETRY_SCHEDULED'" | |
| ) | |
| except Exception as e: | |
| logger.exception("Failed to query pending actions") | |
| return SyncSummary( | |
| result=SyncResult.ALL_FAILED, | |
| error_messages=[f"Database query failed: {str(e)}"] | |
| ) | |
| if not pending_actions: | |
| logger.info("No pending actions to sync") | |
| return SyncSummary(result=SyncResult.SUCCESS) | |
| # Sort by priority (higher first) then timestamp (older first) | |
| pending_actions.sort( | |
| key=lambda x: (-x.get('priority', 1), x['timestamp'])) | |
| synced_count = 0 | |
| failed_count = 0 | |
| expired_count = 0 | |
| error_messages = [] | |
| for action in pending_actions: | |
| action_id = action['id'] | |
| # Check TTL expiry | |
| if time.time() > action['ttl_expiry']: | |
| self._db.update(action_id, status=ActionStatus.EXPIRED.value) | |
| expired_count += 1 | |
| logger.warning(f"Action {action_id} expired") | |
| continue | |
| # Attempt sync | |
| sync_success = self._sync_action(action) | |
| if sync_success: | |
| synced_count += 1 | |
| else: | |
| failed_count += 1 | |
| error_messages.append(f"Action {action_id} failed to sync") | |
| # Determine overall result | |
| if failed_count == 0: | |
| result = SyncResult.SUCCESS | |
| elif synced_count == 0: | |
| result = SyncResult.ALL_FAILED | |
| else: | |
| result = SyncResult.PARTIAL_SUCCESS | |
| return SyncSummary( | |
| result=result, | |
| synced_count=synced_count, | |
| failed_count=failed_count, | |
| expired_count=expired_count, | |
| error_messages=error_messages | |
| ) | |
| def _sync_action(self, action: dict) -> bool: | |
| """Attempt to sync a single action to the cloud.""" | |
| action_id = action['id'] | |
| # Prepare payload (exclude internal fields) | |
| sync_payload = { | |
| "id": action_id, | |
| "type": action['type'], | |
| "payload": action['payload'], | |
| "timestamp": action['timestamp'], | |
| "device_state_hash": action.get('device_state_hash', '') | |
| } | |
| try: | |
| self._api_client.post("/sync", json=sync_payload) | |
| self._db.update(action_id, status=ActionStatus.SYNCED.value) | |
| logger.info(f"Action {action_id} synced successfully") | |
| return True | |
| except TimeoutError: | |
| self._schedule_retry(action_id) | |
| return False | |
| except ConnectionError as e: | |
| logger.error(f"Connection error syncing {action_id}: {str(e)}") | |
| self._schedule_retry(action_id) | |
| return False | |
| except Exception as e: | |
| logger.exception(f"Unexpected error syncing {action_id}") | |
| retry_count = self._retry_counts.get(action_id, 0) | |
| if retry_count >= MAX_RETRY_ATTEMPTS: | |
| self._db.update(action_id, status=ActionStatus.FAILED.value) | |
| logger.error( | |
| f"Action {action_id} permanently failed after {retry_count} retries") | |
| else: | |
| self._schedule_retry(action_id) | |
| return False | |
| def _schedule_retry(self, action_id: str) -> None: | |
| """Schedule an action for retry with exponential backoff.""" | |
| current_count = self._retry_counts.get(action_id, 0) | |
| if current_count >= MAX_RETRY_ATTEMPTS: | |
| self._db.update(action_id, status=ActionStatus.FAILED.value) | |
| logger.error( | |
| f"Action {action_id} exceeded max retries ({MAX_RETRY_ATTEMPTS})" | |
| ) | |
| return | |
| self._retry_counts[action_id] = current_count + 1 | |
| delay = BASE_RETRY_DELAY_SECONDS * (2 ** current_count) | |
| self._db.update( | |
| action_id, | |
| status=ActionStatus.RETRY_SCHEDULED.value, | |
| retry_count=current_count + 1 | |
| ) | |
| logger.info( | |
| f"Action {action_id} scheduled for retry #{current_count + 1} " | |
| f"in {delay} seconds" | |
| ) | |