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
| { | |
| "model_name_or_path": "DeepXR/Helion-2.5-Rnd", | |
| "output_dir": "./checkpoints/helion-2.5-rnd", | |
| "overwrite_output_dir": true, | |
| "do_train": true, | |
| "do_eval": true, | |
| "evaluation_strategy": "steps", | |
| "eval_steps": 500, | |
| "per_device_train_batch_size": 4, | |
| "per_device_eval_batch_size": 4, | |
| "gradient_accumulation_steps": 8, | |
| "learning_rate": 2e-05, | |
| "weight_decay": 0.01, | |
| "adam_beta1": 0.9, | |
| "adam_beta2": 0.999, | |
| "adam_epsilon": 1e-08, | |
| "max_grad_norm": 1.0, | |
| "num_train_epochs": 3, | |
| "max_steps": 150000, | |
| "lr_scheduler_type": "cosine_with_restarts", | |
| "warmup_steps": 2000, | |
| "logging_dir": "./logs", | |
| "logging_strategy": "steps", | |
| "logging_steps": 10, | |
| "save_strategy": "steps", | |
| "save_steps": 1000, | |
| "save_total_limit": 5, | |
| "fp16": false, | |
| "bf16": true, | |
| "dataloader_num_workers": 8, | |
| "dataloader_pin_memory": true, | |
| "gradient_checkpointing": true, | |
| "gradient_checkpointing_kwargs": { | |
| "use_reentrant": false | |
| }, | |
| "deepspeed": { | |
| "train_batch_size": "auto", | |
| "train_micro_batch_size_per_gpu": "auto", | |
| "gradient_accumulation_steps": "auto", | |
| "gradient_clipping": 1.0, | |
| "zero_optimization": { | |
| "stage": 2, | |
| "offload_optimizer": { | |
| "device": "cpu", | |
| "pin_memory": true | |
| }, | |
| "offload_param": { | |
| "device": "cpu", | |
| "pin_memory": true | |
| }, | |
| "overlap_comm": true, | |
| "contiguous_gradients": true, | |
| "reduce_bucket_size": 5e7, | |
| "stage3_prefetch_bucket_size": 5e7, | |
| "stage3_param_persistence_threshold": 1e5 | |
| }, | |
| "fp16": { | |
| "enabled": false | |
| }, | |
| "bf16": { | |
| "enabled": true | |
| }, | |
| "optimizer": { | |
| "type": "AdamW", | |
| "params": { | |
| "lr": "auto", | |
| "betas": "auto", | |
| "eps": "auto", | |
| "weight_decay": "auto" | |
| } | |
| }, | |
| "scheduler": { | |
| "type": "WarmupDecayLR", | |
| "params": { | |
| "warmup_min_lr": "auto", | |
| "warmup_max_lr": "auto", | |
| "warmup_num_steps": "auto", | |
| "total_num_steps": "auto" | |
| } | |
| }, | |
| "zero_allow_untested_optimizer": true, | |
| "wall_clock_breakdown": false | |
| }, | |
| "fsdp": "", | |
| "fsdp_config": {}, | |
| "report_to": ["tensorboard", "wandb"], | |
| "run_name": "helion-2.5-rnd", | |
| "disable_tqdm": false, | |
| "remove_unused_columns": false, | |
| "label_names": ["labels"], | |
| "load_best_model_at_end": true, | |
| "metric_for_best_model": "eval_loss", | |
| "greater_is_better": false, | |
| "ignore_data_skip": false, | |
| "ddp_timeout": 1800, | |
| "torch_compile": false, | |
| "torch_compile_backend": "inductor", | |
| "torch_compile_mode": null, | |
| "optim": "adamw_torch_fused", | |
| "group_by_length": false, | |
| "length_column_name": "length", | |
| "ddp_find_unused_parameters": false, | |
| "ddp_bucket_cap_mb": null, | |
| "ddp_broadcast_buffers": null, | |
| "data_preprocessing": { | |
| "max_seq_length": 131072, | |
| "truncation": true, | |
| "padding": "max_length", | |
| "return_tensors": "pt" | |
| }, | |
| "model_config_updates": { | |
| "use_cache": false, | |
| "attention_dropout": 0.0, | |
| "hidden_dropout": 0.0 | |
| } | |
| } |