Instructions to use DeepXR/Helion-V2.5-Rnd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepXR/Helion-V2.5-Rnd with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="DeepXR/Helion-V2.5-Rnd", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("DeepXR/Helion-V2.5-Rnd", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DeepXR/Helion-V2.5-Rnd with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DeepXR/Helion-V2.5-Rnd"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepXR/Helion-V2.5-Rnd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DeepXR/Helion-V2.5-Rnd

SGLang

How to use DeepXR/Helion-V2.5-Rnd 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-V2.5-Rnd" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepXR/Helion-V2.5-Rnd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-V2.5-Rnd" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DeepXR/Helion-V2.5-Rnd",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DeepXR/Helion-V2.5-Rnd with Docker Model Runner:
```
docker model run hf.co/DeepXR/Helion-V2.5-Rnd
```

Trouter-Library commited on Dec 11, 2025

Commit

7d85879

verified ·

1 Parent(s): 2bad6cc

Update README.md

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@@ -91,33 +91,6 @@ The model incorporates several key architectural improvements:
 - Dynamic learning rate scheduling with restarts
 - Careful hyperparameter tuning for stability at scale
-## Performance Benchmarks
-### Reasoning and Knowledge
-| Benchmark | Score | Description |
-|-----------|-------|-------------|
-| MMLU | 84.7% | Massive Multitask Language Understanding |
-| ARC Challenge | 83.4% | Advanced reasoning and comprehension |
-| HellaSwag | 88.9% | Common sense inference |
-| WinoGrande | 82.3% | Commonsense reasoning |
-| TruthfulQA | 61.2% | Truthfulness in question answering |
-### Mathematical Reasoning
-| Benchmark | Score | Description |
-|-----------|-------|-------------|
-| GSM8K | 89.2% | Grade school mathematics |
-| MATH | 56.7% | Competition-level mathematics |
-| Minerva Math | 53.4% | Advanced mathematical reasoning |
-### Code Generation
-| Benchmark | Score | Description |
-|-----------|-------|-------------|
-| HumanEval | 75.6% | Python code generation |
-| MBPP | 72.3% | Basic Python programming |
-| DS-1000 | 64.5% | Data science code completion |
 ### Context Understanding

 - Dynamic learning rate scheduling with restarts
 - Careful hyperparameter tuning for stability at scale
 ### Context Understanding