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
| { | |
| "model_type": "helion-osc", | |
| "architectures": ["HelionOSCForCausalLM"], | |
| "vocab_size": 102400, | |
| "hidden_size": 5120, | |
| "num_hidden_layers": 48, | |
| "num_attention_heads": 40, | |
| "num_key_value_heads": 8, | |
| "intermediate_size": 18432, | |
| "hidden_act": "swiglu", | |
| "max_position_embeddings": 262144, | |
| "initializer_range": 0.02, | |
| "rms_norm_eps": 1e-6, | |
| "use_cache": true, | |
| "pad_token_id": 0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "tie_word_embeddings": false, | |
| "rope_theta": 10000000.0, | |
| "rope_scaling": { | |
| "type": "linear", | |
| "factor": 32.0 | |
| }, | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "mlp_bias": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.40.0", | |
| "model_version": "1.0", | |
| "use_flash_attention": true, | |
| "sliding_window": null, | |
| "gradient_checkpointing": false, | |
| "task_specific_params": { | |
| "code_generation": { | |
| "max_length": 4096, | |
| "temperature": 0.7, | |
| "top_p": 0.95, | |
| "top_k": 50, | |
| "do_sample": true, | |
| "repetition_penalty": 1.05, | |
| "length_penalty": 1.0 | |
| }, | |
| "mathematical_reasoning": { | |
| "max_length": 2048, | |
| "temperature": 0.3, | |
| "top_p": 0.9, | |
| "top_k": 40, | |
| "do_sample": false, | |
| "repetition_penalty": 1.0, | |
| "length_penalty": 1.2 | |
| }, | |
| "code_completion": { | |
| "max_length": 1024, | |
| "temperature": 0.6, | |
| "top_p": 0.92, | |
| "top_k": 45, | |
| "do_sample": true, | |
| "repetition_penalty": 1.03, | |
| "stop_sequences": ["\n\n", "```", "###"] | |
| }, | |
| "algorithm_design": { | |
| "max_length": 3072, | |
| "temperature": 0.5, | |
| "top_p": 0.93, | |
| "top_k": 50, | |
| "do_sample": true, | |
| "repetition_penalty": 1.08 | |
| }, | |
| "debugging": { | |
| "max_length": 2048, | |
| "temperature": 0.4, | |
| "top_p": 0.88, | |
| "do_sample": false, | |
| "repetition_penalty": 1.0 | |
| } | |
| }, | |
| "specialization": { | |
| "domain": "coding_and_mathematics", | |
| "primary_focus": "code_generation_with_mathematical_reasoning", | |
| "verification_enabled": true, | |
| "step_by_step_reasoning": true, | |
| "languages_supported": [ | |
| "python", | |
| "javascript", | |
| "typescript", | |
| "java", | |
| "c", | |
| "cpp", | |
| "csharp", | |
| "go", | |
| "rust", | |
| "ruby", | |
| "php", | |
| "swift", | |
| "kotlin", | |
| "scala", | |
| "r", | |
| "sql", | |
| "bash", | |
| "shell" | |
| ], | |
| "features": [ | |
| "code_generation", | |
| "code_completion", | |
| "bug_detection", | |
| "bug_fixing", | |
| "mathematical_reasoning", | |
| "theorem_proving", | |
| "algorithm_design", | |
| "algorithm_optimization", | |
| "code_refactoring", | |
| "documentation_generation", | |
| "test_generation", | |
| "complexity_analysis" | |
| ], | |
| "mathematical_capabilities": [ | |
| "arithmetic", | |
| "algebra", | |
| "calculus", | |
| "discrete_mathematics", | |
| "linear_algebra", | |
| "probability", | |
| "statistics", | |
| "number_theory", | |
| "graph_theory", | |
| "combinatorics" | |
| ] | |
| }, | |
| "training_config": { | |
| "training_precision": "bf16", | |
| "optimizer": "adamw", | |
| "learning_rate": 2e-5, | |
| "warmup_steps": 2000, | |
| "weight_decay": 0.01, | |
| "max_grad_norm": 1.0 | |
| }, | |
| "quantization_config": { | |
| "quant_method": "bitsandbytes", | |
| "load_in_8bit": false, | |
| "load_in_4bit": false, | |
| "bnb_4bit_compute_dtype": "bfloat16", | |
| "bnb_4bit_use_double_quant": true, | |
| "bnb_4bit_quant_type": "nf4" | |
| }, | |
| "generation_config": { | |
| "temperature": 0.7, | |
| "top_p": 0.95, | |
| "top_k": 50, | |
| "do_sample": true, | |
| "max_new_tokens": 2048, | |
| "min_new_tokens": 1, | |
| "num_beams": 1, | |
| "early_stopping": false, | |
| "no_repeat_ngram_size": 3, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "diversity_penalty": 0.0, | |
| "repetition_penalty": 1.05, | |
| "length_penalty": 1.0, | |
| "exponential_decay_length_penalty": null | |
| }, | |
| "special_tokens": { | |
| "pad_token": "<|pad|>", | |
| "bos_token": "<|begin_of_text|>", | |
| "eos_token": "<|end_of_text|>", | |
| "unk_token": "<|unk|>", | |
| "code_start_token": "<|code_start|>", | |
| "code_end_token": "<|code_end|>", | |
| "math_start_token": "<|math_start|>", | |
| "math_end_token": "<|math_end|>", | |
| "reasoning_start_token": "<|reasoning_start|>", | |
| "reasoning_end_token": "<|reasoning_end|>", | |
| "explanation_start_token": "<|explanation_start|>", | |
| "explanation_end_token": "<|explanation_end|>" | |
| }, | |
| "supported_frameworks": [ | |
| "pytorch", | |
| "tensorflow", | |
| "onnx", | |
| "jax" | |
| ], | |
| "evaluation_metrics": { | |
| "humaneval_pass_at_1": 0.852, | |
| "humaneval_pass_at_10": 0.928, | |
| "mbpp_pass_at_1": 0.795, | |
| "mbpp_pass_at_10": 0.891, | |
| "gsm8k_accuracy": 0.785, | |
| "math_accuracy": 0.623, | |
| "apps_accuracy": 0.412 | |
| }, | |
| "hardware_requirements": { | |
| "minimum_vram_gb": 16, | |
| "recommended_vram_gb": 24, | |
| "minimum_ram_gb": 32, | |
| "recommended_ram_gb": 64, | |
| "cpu_cores": 8, | |
| "gpu_support": true, | |
| "multi_gpu_support": true, | |
| "cpu_only_support": true | |
| }, | |
| "deployment_options": { | |
| "inference_frameworks": [ | |
| "vllm", | |
| "text-generation-inference", | |
| "ollama", | |
| "llama.cpp" | |
| ], | |
| "optimization_support": [ | |
| "quantization", | |
| "pruning", | |
| "distillation", | |
| "tensorrt", | |
| "onnx_runtime" | |
| ] | |
| } | |
| } |