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 config.json
Browse files- config.json +61 -0
config.json
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{
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"model_type": "helion-osc",
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"architectures": ["HelionOSCForCausalLM"],
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"vocab_size": 50280,
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"hidden_size": 4096,
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"num_hidden_layers": 32,
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"num_attention_heads": 32,
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"num_key_value_heads": 8,
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"intermediate_size": 14336,
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"hidden_act": "silu",
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"max_position_embeddings": 8192,
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"initializer_range": 0.02,
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"rms_norm_eps": 1e-6,
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"use_cache": true,
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"pad_token_id": 0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"tie_word_embeddings": false,
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"rope_theta": 10000.0,
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"rope_scaling": null,
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"attention_bias": false,
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"attention_dropout": 0.0,
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"mlp_bias": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.36.0",
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"task_specific_params": {
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"code_generation": {
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"max_length": 2048,
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"temperature": 0.7,
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"top_p": 0.95,
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"do_sample": true
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},
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"mathematical_reasoning": {
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"max_length": 1024,
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"temperature": 0.3,
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"top_p": 0.9,
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"do_sample": false
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}
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},
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"specialization": {
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"domain": "coding_and_mathematics",
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"languages_supported": [
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"python",
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"javascript",
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"typescript",
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"java",
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"c++",
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"rust",
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"go",
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"sql"
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],
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"features": [
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"code_generation",
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"code_completion",
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"bug_detection",
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"mathematical_reasoning",
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"algorithm_design",
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"code_optimization"
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]
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}
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}
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