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 model_card.yaml
Browse files- model_card.yaml +55 -0
model_card.yaml
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---
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language:
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- en
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- code
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license: apache-2.0
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library_name: transformers
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tags:
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- code
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- mathematics
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- reasoning
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- causal-lm
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- text-generation
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| 13 |
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- python
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| 14 |
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- javascript
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| 15 |
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- algorithm
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- optimization
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datasets:
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- code_repositories
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- mathematical_texts
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pipeline_tag: text-generation
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model-index:
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- name: Helion-OSC
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results:
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- task:
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type: text-generation
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name: Code Generation
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metrics:
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| 28 |
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- type: pass@1
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| 29 |
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value: 85.2
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| 30 |
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name: HumanEval Pass@1
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- type: pass@10
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| 32 |
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value: 92.8
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| 33 |
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name: HumanEval Pass@10
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| 34 |
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- task:
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type: text-generation
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| 36 |
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name: Mathematical Reasoning
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metrics:
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- type: accuracy
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value: 78.5
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name: GSM8K Accuracy
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widget:
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- text: "Write a Python function to implement binary search:"
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example_title: "Code Generation"
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- text: "Solve: If f(x) = 2x^2 + 3x - 5, find f'(x)"
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example_title: "Mathematical Reasoning"
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- text: "Optimize this code: for i in range(len(arr)): for j in range(len(arr)):"
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example_title: "Code Optimization"
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inference:
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parameters:
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max_length: 1024
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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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base_model: null
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base_model_relation: null
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