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", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
Create inference/config.json
Browse files- inference/config.json +112 -0
inference/config.json
ADDED
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| 1 |
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{
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| 2 |
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"_name_or_path": "DeepXR/Helion-OSC",
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| 3 |
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"architectures": [
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| 4 |
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"HelionOSCForCausalLM"
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| 5 |
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],
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| 6 |
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"attention_bias": false,
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| 7 |
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"attention_dropout": 0.0,
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| 8 |
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"bos_token_id": 1,
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| 9 |
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"eos_token_id": 2,
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| 10 |
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"pad_token_id": 0,
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| 11 |
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"hidden_act": "swiglu",
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| 12 |
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"hidden_size": 8192,
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| 13 |
+
"initializer_range": 0.02,
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| 14 |
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"intermediate_size": 28672,
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| 15 |
+
"max_position_embeddings": 32768,
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| 16 |
+
"model_type": "helion-osc",
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| 17 |
+
"num_attention_heads": 64,
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| 18 |
+
"num_hidden_layers": 80,
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| 19 |
+
"num_key_value_heads": 8,
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| 20 |
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"pretraining_tp": 1,
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| 21 |
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"rms_norm_eps": 1e-5,
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| 22 |
+
"rope_scaling": {
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| 23 |
+
"factor": 8.0,
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| 24 |
+
"high_freq_factor": 4.0,
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| 25 |
+
"low_freq_factor": 1.0,
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| 26 |
+
"original_max_position_embeddings": 8192,
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| 27 |
+
"rope_type": "llama3"
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| 28 |
+
},
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| 29 |
+
"rope_theta": 500000.0,
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| 30 |
+
"tie_word_embeddings": false,
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| 31 |
+
"torch_dtype": "bfloat16",
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| 32 |
+
"transformers_version": "4.40.0",
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| 33 |
+
"use_cache": true,
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| 34 |
+
"vocab_size": 128256,
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| 35 |
+
"sliding_window": null,
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| 36 |
+
"mlp_bias": false,
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| 37 |
+
"head_dim": 128,
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| 38 |
+
"quantization_config": null,
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| 39 |
+
"gradient_checkpointing": false,
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| 40 |
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"use_flash_attention_2": true,
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| 41 |
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"attn_implementation": "flash_attention_2",
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| 42 |
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"rope_interleaved": false,
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| 43 |
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"output_attentions": false,
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| 44 |
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"output_hidden_states": false,
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| 45 |
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"use_return_dict": true,
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| 46 |
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"torchscript": false,
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| 47 |
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"use_bfloat16": true,
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| 48 |
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"tf_legacy_loss": false,
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| 49 |
+
"pruned_heads": {},
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| 50 |
+
"chunk_size_feed_forward": 0,
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| 51 |
+
"is_encoder_decoder": false,
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| 52 |
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"is_decoder": false,
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| 53 |
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"cross_attention_hidden_size": null,
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| 54 |
+
"add_cross_attention": false,
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| 55 |
+
"tie_encoder_decoder": false,
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| 56 |
+
"max_length": 32768,
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| 57 |
+
"min_length": 0,
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| 58 |
+
"do_sample": false,
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| 59 |
+
"early_stopping": false,
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| 60 |
+
"num_beams": 1,
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| 61 |
+
"num_beam_groups": 1,
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| 62 |
+
"diversity_penalty": 0.0,
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| 63 |
+
"temperature": 0.7,
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| 64 |
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"top_k": 50,
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| 65 |
+
"top_p": 0.95,
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| 66 |
+
"typical_p": 1.0,
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| 67 |
+
"repetition_penalty": 1.05,
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| 68 |
+
"length_penalty": 1.0,
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| 69 |
+
"no_repeat_ngram_size": 3,
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| 70 |
+
"encoder_no_repeat_ngram_size": 0,
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| 71 |
+
"bad_words_ids": null,
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| 72 |
+
"num_return_sequences": 1,
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| 73 |
+
"output_scores": false,
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| 74 |
+
"return_dict_in_generate": false,
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| 75 |
+
"forced_bos_token_id": null,
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| 76 |
+
"forced_eos_token_id": null,
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| 77 |
+
"remove_invalid_values": false,
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| 78 |
+
"exponential_decay_length_penalty": null,
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| 79 |
+
"suppress_tokens": null,
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| 80 |
+
"begin_suppress_tokens": null,
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| 81 |
+
"architectures_info": {
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| 82 |
+
"model_description": "Helion-OSC is a large-scale transformer model optimized for code generation and mathematical reasoning",
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| 83 |
+
"total_parameters": "405B",
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| 84 |
+
"active_parameters": "37B",
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| 85 |
+
"num_experts": 160,
|
| 86 |
+
"experts_per_token": 6,
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| 87 |
+
"shared_expert_intermediate_size": 14336,
|
| 88 |
+
"moe_intermediate_size": 4096,
|
| 89 |
+
"routed_scaling_factor": 1.0,
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| 90 |
+
"scoring_func": "softmax",
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| 91 |
+
"aux_loss_alpha": 0.001,
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| 92 |
+
"seq_aux": true,
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| 93 |
+
"topk_method": "gready",
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| 94 |
+
"n_group": 1,
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| 95 |
+
"topk_group": 3,
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| 96 |
+
"num_shared_experts": 2,
|
| 97 |
+
"norm_topk_prob": false,
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| 98 |
+
"route_scale": 1.0,
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| 99 |
+
"moe_layer_freq": 1,
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| 100 |
+
"first_k_dense_replace": 1,
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| 101 |
+
"architecture_type": "MoE-Transformer"
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| 102 |
+
},
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| 103 |
+
"safetensors_configuration": {
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| 104 |
+
"total_shards": 116,
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| 105 |
+
"shard_size_gb": 2.8,
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| 106 |
+
"total_size_gb": 324.8,
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| 107 |
+
"weight_map_file": "model.safetensors.index.json",
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| 108 |
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"shard_pattern": "model-{:05d}-of-{:05d}.safetensors",
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| 109 |
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"max_shard_size": "3GB",
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| 110 |
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"metadata_included": true
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| 111 |
+
}
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| 112 |
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}
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