Text Generation
Transformers
Safetensors
llama
research
code
mathematics
reasoning
multilingual
long-context
custom_code
text-generation-inference
Instructions to use DeepXR/Helion-V2.5-Rnd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepXR/Helion-V2.5-Rnd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepXR/Helion-V2.5-Rnd", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use DeepXR/Helion-V2.5-Rnd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DeepXR/Helion-V2.5-Rnd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-V2.5-Rnd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DeepXR/Helion-V2.5-Rnd
- SGLang
How to use DeepXR/Helion-V2.5-Rnd 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 "DeepXR/Helion-V2.5-Rnd" \ --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": "DeepXR/Helion-V2.5-Rnd", "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 "DeepXR/Helion-V2.5-Rnd" \ --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": "DeepXR/Helion-V2.5-Rnd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DeepXR/Helion-V2.5-Rnd with Docker Model Runner:
docker model run hf.co/DeepXR/Helion-V2.5-Rnd
| { | |
| "monitoring": { | |
| "enabled": true, | |
| "interval_seconds": 15, | |
| "retention_days": 30 | |
| }, | |
| "metrics": { | |
| "system": { | |
| "enabled": true, | |
| "collect": [ | |
| "cpu_usage", | |
| "memory_usage", | |
| "disk_usage", | |
| "network_io" | |
| ] | |
| }, | |
| "gpu": { | |
| "enabled": true, | |
| "collect": [ | |
| "gpu_utilization", | |
| "gpu_memory_used", | |
| "gpu_memory_total", | |
| "gpu_temperature", | |
| "gpu_power_usage" | |
| ], | |
| "alert_thresholds": { | |
| "temperature_celsius": 85, | |
| "memory_utilization_percent": 95, | |
| "power_watts": 400 | |
| } | |
| }, | |
| "model": { | |
| "enabled": true, | |
| "collect": [ | |
| "requests_per_second", | |
| "tokens_per_second", | |
| "average_latency_ms", | |
| "p50_latency_ms", | |
| "p95_latency_ms", | |
| "p99_latency_ms", | |
| "error_rate", | |
| "active_connections", | |
| "queue_depth" | |
| ] | |
| }, | |
| "inference": { | |
| "enabled": true, | |
| "collect": [ | |
| "prompt_tokens", | |
| "completion_tokens", | |
| "total_tokens", | |
| "generation_time_ms", | |
| "preprocessing_time_ms", | |
| "postprocessing_time_ms" | |
| ] | |
| } | |
| }, | |
| "alerts": { | |
| "enabled": true, | |
| "channels": [ | |
| "email", | |
| "slack", | |
| "pagerduty" | |
| ], | |
| "rules": [ | |
| { | |
| "name": "high_error_rate", | |
| "condition": "error_rate > 0.05", | |
| "duration_seconds": 300, | |
| "severity": "critical", | |
| "message": "Error rate exceeded 5% for 5 minutes" | |
| }, | |
| { | |
| "name": "high_latency", | |
| "condition": "p95_latency_ms > 5000", | |
| "duration_seconds": 180, | |
| "severity": "warning", | |
| "message": "P95 latency exceeded 5 seconds" | |
| }, | |
| { | |
| "name": "gpu_temperature_high", | |
| "condition": "gpu_temperature > 85", | |
| "duration_seconds": 60, | |
| "severity": "critical", | |
| "message": "GPU temperature critically high" | |
| }, | |
| { | |
| "name": "memory_pressure", | |
| "condition": "gpu_memory_used / gpu_memory_total > 0.95", | |
| "duration_seconds": 300, | |
| "severity": "warning", | |
| "message": "GPU memory utilization above 95%" | |
| }, | |
| { | |
| "name": "low_throughput", | |
| "condition": "tokens_per_second < 10", | |
| "duration_seconds": 600, | |
| "severity": "warning", | |
| "message": "Throughput below 10 tokens/second" | |
| } | |
| ] | |
| }, | |
| "logging": { | |
| "level": "INFO", | |
| "format": "json", | |
| "outputs": [ | |
| { | |
| "type": "file", | |
| "path": "./logs/monitoring.log", | |
| "rotation": "daily", | |
| "retention_days": 30 | |
| }, | |
| { | |
| "type": "stdout", | |
| "enabled": true | |
| }, | |
| { | |
| "type": "elasticsearch", | |
| "enabled": false, | |
| "host": "localhost:9200", | |
| "index": "helion-metrics" | |
| } | |
| ] | |
| }, | |
| "prometheus": { | |
| "enabled": true, | |
| "port": 8001, | |
| "path": "/metrics", | |
| "namespace": "helion", | |
| "subsystem": "inference", | |
| "labels": { | |
| "model": "Helion-2.5-Rnd", | |
| "version": "2.5.0-rnd", | |
| "environment": "production" | |
| } | |
| }, | |
| "grafana": { | |
| "enabled": true, | |
| "dashboards": [ | |
| { | |
| "name": "Helion Overview", | |
| "file": "./monitoring/dashboards/overview.json", | |
| "refresh": "30s" | |
| }, | |
| { | |
| "name": "GPU Metrics", | |
| "file": "./monitoring/dashboards/gpu.json", | |
| "refresh": "15s" | |
| }, | |
| { | |
| "name": "Inference Performance", | |
| "file": "./monitoring/dashboards/inference.json", | |
| "refresh": "30s" | |
| } | |
| ] | |
| }, | |
| "health_checks": { | |
| "enabled": true, | |
| "endpoint": "/health", | |
| "interval_seconds": 30, | |
| "timeout_seconds": 10, | |
| "checks": [ | |
| { | |
| "name": "model_loaded", | |
| "type": "internal", | |
| "critical": true | |
| }, | |
| { | |
| "name": "gpu_available", | |
| "type": "internal", | |
| "critical": true | |
| }, | |
| { | |
| "name": "inference_responsive", | |
| "type": "endpoint", | |
| "url": "http://localhost:8000/v1/models", | |
| "critical": false | |
| } | |
| ] | |
| }, | |
| "tracing": { | |
| "enabled": true, | |
| "sample_rate": 0.1, | |
| "exporter": "jaeger", | |
| "endpoint": "http://localhost:14268/api/traces" | |
| }, | |
| "profiling": { | |
| "enabled": false, | |
| "interval_seconds": 3600, | |
| "duration_seconds": 300, | |
| "output_dir": "./profiling" | |
| } | |
| } |