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
Create generation_config.json
Browse files- generation_config.json +91 -0
generation_config.json
ADDED
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{
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"_from_model_config": true,
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"bos_token_id": 128000,
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"eos_token_id": 128009,
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"pad_token_id": 128001,
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"transformers_version": "4.40.0",
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"model_type": "llama",
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"architectures": [
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"LlamaForCausalLM"
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],
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"do_sample": true,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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| 15 |
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"repetition_penalty": 1.1,
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"max_length": 131072,
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"max_new_tokens": 4096,
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"min_length": 0,
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"min_new_tokens": null,
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"early_stopping": false,
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"num_beams": 1,
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"num_beam_groups": 1,
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"diversity_penalty": 0.0,
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"length_penalty": 1.0,
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"no_repeat_ngram_size": 0,
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| 26 |
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"encoder_no_repeat_ngram_size": 0,
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"bad_words_ids": null,
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"forced_bos_token_id": null,
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"forced_eos_token_id": null,
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"remove_invalid_values": false,
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"exponential_decay_length_penalty": null,
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"suppress_tokens": null,
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"begin_suppress_tokens": null,
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"forced_decoder_ids": null,
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"num_return_sequences": 1,
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"output_attentions": false,
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"output_hidden_states": false,
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"output_scores": false,
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"return_dict_in_generate": true,
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"use_cache": true,
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"typical_p": 1.0,
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"epsilon_cutoff": 0.0,
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"eta_cutoff": 0.0,
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"renormalize_logits": false,
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"constraints": null,
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"guidance_scale": null,
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"low_memory": null,
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"watermarking_config": null,
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"presets": {
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"creative": {
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"temperature": 0.9,
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"top_p": 0.95,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"description": "High creativity for stories, brainstorming, creative writing"
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},
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"precise": {
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"temperature": 0.3,
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"top_p": 0.85,
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"top_k": 40,
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"repetition_penalty": 1.15,
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"description": "Low randomness for factual, technical, or code generation"
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},
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"balanced": {
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"description": "Balanced for general purpose conversations"
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},
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"deterministic": {
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"temperature": 0.0,
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"top_p": 1.0,
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"top_k": 1,
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"repetition_penalty": 1.0,
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"do_sample": false,
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"description": "Fully deterministic output, same input = same output"
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}
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},
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"stop_sequences": [
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"<|im_end|>",
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"<|endoftext|>",
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"</s>"
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],
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"chat_format": {
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"system": "<|im_start|>system\n{content}<|im_end|>\n",
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"user": "<|im_start|>user\n{content}<|im_end|>\n",
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"assistant": "<|im_start|>assistant\n{content}<|im_end|>\n",
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"prompt_suffix": "<|im_start|>assistant\n"
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}
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}
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