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
File size: 2,411 Bytes
9e7df3d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 | {
"_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"
}
} |