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
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"monitoring": {
"enabled": true,
"interval_seconds": 15,
"retention_days": 30
},
"metrics": {
"system": {
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"collect": [
"cpu_usage",
"memory_usage",
"disk_usage",
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},
"gpu": {
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"collect": [
"gpu_utilization",
"gpu_memory_used",
"gpu_memory_total",
"gpu_temperature",
"gpu_power_usage"
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"alert_thresholds": {
"temperature_celsius": 85,
"memory_utilization_percent": 95,
"power_watts": 400
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},
"model": {
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"collect": [
"requests_per_second",
"tokens_per_second",
"average_latency_ms",
"p50_latency_ms",
"p95_latency_ms",
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"error_rate",
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"collect": [
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"channels": [
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"rules": [
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"severity": "critical",
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{
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"severity": "warning",
"message": "P95 latency exceeded 5 seconds"
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{
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"severity": "critical",
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{
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"severity": "warning",
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{
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"condition": "tokens_per_second < 10",
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"logging": {
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{
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"rotation": "daily",
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},
{
"type": "stdout",
"enabled": true
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{
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"host": "localhost:9200",
"index": "helion-metrics"
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]
},
"prometheus": {
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"namespace": "helion",
"subsystem": "inference",
"labels": {
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"version": "2.5.0-rnd",
"environment": "production"
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"grafana": {
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"dashboards": [
{
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"file": "./monitoring/dashboards/overview.json",
"refresh": "30s"
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{
"name": "GPU Metrics",
"file": "./monitoring/dashboards/gpu.json",
"refresh": "15s"
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{
"name": "Inference Performance",
"file": "./monitoring/dashboards/inference.json",
"refresh": "30s"
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"checks": [
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"type": "internal",
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{
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{
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"type": "endpoint",
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"tracing": {
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"sample_rate": 0.1,
"exporter": "jaeger",
"endpoint": "http://localhost:14268/api/traces"
},
"profiling": {
"enabled": false,
"interval_seconds": 3600,
"duration_seconds": 300,
"output_dir": "./profiling"
}
} |