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
| { | |
| "_name_or_path": "DeepXR/Helion-OSC", | |
| "architectures": [ | |
| "HelionOSCForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "pad_token_id": 0, | |
| "hidden_act": "swiglu", | |
| "hidden_size": 8192, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 28672, | |
| "max_position_embeddings": 32768, | |
| "model_type": "helion-osc", | |
| "num_attention_heads": 64, | |
| "num_hidden_layers": 80, | |
| "num_key_value_heads": 8, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-5, | |
| "rope_scaling": { | |
| "factor": 8.0, | |
| "high_freq_factor": 4.0, | |
| "low_freq_factor": 1.0, | |
| "original_max_position_embeddings": 8192, | |
| "rope_type": "llama3" | |
| }, | |
| "rope_theta": 500000.0, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.40.0", | |
| "use_cache": true, | |
| "vocab_size": 128256, | |
| "sliding_window": null, | |
| "mlp_bias": false, | |
| "head_dim": 128, | |
| "quantization_config": null, | |
| "gradient_checkpointing": false, | |
| "use_flash_attention_2": true, | |
| "attn_implementation": "flash_attention_2", | |
| "rope_interleaved": false, | |
| "output_attentions": false, | |
| "output_hidden_states": false, | |
| "use_return_dict": true, | |
| "torchscript": false, | |
| "use_bfloat16": true, | |
| "tf_legacy_loss": false, | |
| "pruned_heads": {}, | |
| "chunk_size_feed_forward": 0, | |
| "is_encoder_decoder": false, | |
| "is_decoder": false, | |
| "cross_attention_hidden_size": null, | |
| "add_cross_attention": false, | |
| "tie_encoder_decoder": false, | |
| "max_length": 32768, | |
| "min_length": 0, | |
| "do_sample": false, | |
| "early_stopping": false, | |
| "num_beams": 1, | |
| "num_beam_groups": 1, | |
| "diversity_penalty": 0.0, | |
| "temperature": 0.7, | |
| "top_k": 50, | |
| "top_p": 0.95, | |
| "typical_p": 1.0, | |
| "repetition_penalty": 1.05, | |
| "length_penalty": 1.0, | |
| "no_repeat_ngram_size": 3, | |
| "encoder_no_repeat_ngram_size": 0, | |
| "bad_words_ids": null, | |
| "num_return_sequences": 1, | |
| "output_scores": false, | |
| "return_dict_in_generate": false, | |
| "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, | |
| "architectures_info": { | |
| "model_description": "Helion-OSC is a large-scale transformer model optimized for code generation and mathematical reasoning", | |
| "total_parameters": "405B", | |
| "active_parameters": "37B", | |
| "num_experts": 160, | |
| "experts_per_token": 6, | |
| "shared_expert_intermediate_size": 14336, | |
| "moe_intermediate_size": 4096, | |
| "routed_scaling_factor": 1.0, | |
| "scoring_func": "softmax", | |
| "aux_loss_alpha": 0.001, | |
| "seq_aux": true, | |
| "topk_method": "gready", | |
| "n_group": 1, | |
| "topk_group": 3, | |
| "num_shared_experts": 2, | |
| "norm_topk_prob": false, | |
| "route_scale": 1.0, | |
| "moe_layer_freq": 1, | |
| "first_k_dense_replace": 1, | |
| "architecture_type": "MoE-Transformer" | |
| }, | |
| "safetensors_configuration": { | |
| "total_shards": 116, | |
| "shard_size_gb": 3.01, | |
| "total_size_gb": 349.16, | |
| "weight_map_file": "model.safetensors.index.json", | |
| "shard_pattern": "model-{:05d}-of-{:05d}.safetensors", | |
| "max_shard_size": "3.1GB", | |
| "metadata_included": true | |
| } | |
| } |