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
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 128000, | |
| "eos_token_id": 128009, | |
| "pad_token_id": 128001, | |
| "transformers_version": "4.40.0", | |
| "model_type": "llama", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "do_sample": true, | |
| "temperature": 0.7, | |
| "top_p": 0.9, | |
| "top_k": 50, | |
| "repetition_penalty": 1.1, | |
| "max_length": 131072, | |
| "max_new_tokens": 4096, | |
| "min_length": 0, | |
| "min_new_tokens": null, | |
| "early_stopping": false, | |
| "num_beams": 1, | |
| "num_beam_groups": 1, | |
| "diversity_penalty": 0.0, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 0, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "bad_words_ids": null, | |
| "forced_bos_token_id": null, | |
| "forced_eos_token_id": null, | |
| "remove_invalid_values": false, | |
| "exponential_decay_length_penalty": null, | |
| "suppress_tokens": null, | |
| "begin_suppress_tokens": null, | |
| "forced_decoder_ids": null, | |
| "num_return_sequences": 1, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "output_scores": false, | |
| "return_dict_in_generate": true, | |
| "use_cache": true, | |
| "typical_p": 1.0, | |
| "epsilon_cutoff": 0.0, | |
| "eta_cutoff": 0.0, | |
| "renormalize_logits": false, | |
| "constraints": null, | |
| "guidance_scale": null, | |
| "low_memory": null, | |
| "watermarking_config": null, | |
| "presets": { | |
| "creative": { | |
| "temperature": 0.9, | |
| "top_p": 0.95, | |
| "top_k": 50, | |
| "repetition_penalty": 1.1, | |
| "description": "High creativity for stories, brainstorming, creative writing" | |
| }, | |
| "precise": { | |
| "temperature": 0.3, | |
| "top_p": 0.85, | |
| "top_k": 40, | |
| "repetition_penalty": 1.15, | |
| "description": "Low randomness for factual, technical, or code generation" | |
| }, | |
| "balanced": { | |
| "temperature": 0.7, | |
| "top_p": 0.9, | |
| "top_k": 50, | |
| "repetition_penalty": 1.1, | |
| "description": "Balanced for general purpose conversations" | |
| }, | |
| "deterministic": { | |
| "temperature": 0.0, | |
| "top_p": 1.0, | |
| "top_k": 1, | |
| "repetition_penalty": 1.0, | |
| "do_sample": false, | |
| "description": "Fully deterministic output, same input = same output" | |
| } | |
| }, | |
| "stop_sequences": [ | |
| "<|im_end|>", | |
| "<|endoftext|>", | |
| "</s>" | |
| ], | |
| "chat_format": { | |
| "system": "<|im_start|>system\n{content}<|im_end|>\n", | |
| "user": "<|im_start|>user\n{content}<|im_end|>\n", | |
| "assistant": "<|im_start|>assistant\n{content}<|im_end|>\n", | |
| "prompt_suffix": "<|im_start|>assistant\n" | |
| } | |
| } |