Text Generation
Transformers
Safetensors
English
code
helion-osc
mathematics
reasoning
algorithm
causal-lm
conversational
bitsandbytes
Instructions to use DeepXR/Helion-OSC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DeepXR/Helion-OSC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DeepXR/Helion-OSC") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DeepXR/Helion-OSC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DeepXR/Helion-OSC with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DeepXR/Helion-OSC" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-OSC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DeepXR/Helion-OSC
- SGLang
How to use DeepXR/Helion-OSC 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-OSC" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-OSC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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-OSC" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DeepXR/Helion-OSC", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DeepXR/Helion-OSC with Docker Model Runner:
docker model run hf.co/DeepXR/Helion-OSC
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"_from_model_config": true,
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 0,
"transformers_version": "4.40.0",
"model_type": "helion-osc",
"do_sample": true,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 50,
"repetition_penalty": 1.05,
"length_penalty": 1.0,
"no_repeat_ngram_size": 3,
"encoder_no_repeat_ngram_size": 0,
"num_beams": 1,
"num_beam_groups": 1,
"diversity_penalty": 0.0,
"early_stopping": false,
"max_length": 16384,
"max_new_tokens": null,
"min_length": 0,
"min_new_tokens": null,
"exponential_decay_length_penalty": null,
"remove_invalid_values": false,
"output_scores": false,
"output_attentions": false,
"output_hidden_states": false,
"return_dict_in_generate": false,
"forced_bos_token_id": null,
"forced_eos_token_id": null,
"suppress_tokens": null,
"begin_suppress_tokens": null,
"use_cache": true,
"task_profiles": {
"code_generation_creative": {
"temperature": 0.8,
"top_p": 0.95,
"top_k": 60,
"repetition_penalty": 1.08,
"max_new_tokens": 4096,
"description": "Creative code generation with diverse solutions"
},
"code_generation_precise": {
"temperature": 0.3,
"top_p": 0.85,
"top_k": 40,
"repetition_penalty": 1.02,
"max_new_tokens": 4096,
"do_sample": false,
"description": "Precise, deterministic code generation"
},
"mathematical_proof": {
"temperature": 0.2,
"top_p": 0.8,
"top_k": 30,
"repetition_penalty": 1.0,
"max_new_tokens": 3072,
"do_sample": false,
"description": "Rigorous mathematical proofs and derivations"
},
"algorithm_optimization": {
"temperature": 0.5,
"top_p": 0.92,
"top_k": 50,
"repetition_penalty": 1.1,
"max_new_tokens": 3072,
"description": "Algorithm design with optimization focus"
},
"code_explanation": {
"temperature": 0.6,
"top_p": 0.9,
"top_k": 45,
"repetition_penalty": 1.05,
"max_new_tokens": 2048,
"description": "Detailed code explanations and documentation"
},
"debugging_analysis": {
"temperature": 0.4,
"top_p": 0.88,
"top_k": 40,
"repetition_penalty": 1.0,
"max_new_tokens": 2048,
"do_sample": false,
"description": "Systematic debugging and error analysis"
},
"competitive_programming": {
"temperature": 0.65,
"top_p": 0.93,
"top_k": 55,
"repetition_penalty": 1.12,
"max_new_tokens": 2048,
"description": "Competitive programming solutions"
},
"system_design": {
"temperature": 0.7,
"top_p": 0.94,
"top_k": 55,
"repetition_penalty": 1.06,
"max_new_tokens": 4096,
"description": "System architecture and design patterns"
}
}
} |