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 3, 2025

Commit

f4cefbc

verified ·

1 Parent(s): d9adc0d

Update training_config.json

Browse files

Files changed (1) hide show

training_config.json +1 -28

training_config.json CHANGED Viewed

@@ -1,5 +1,5 @@
 {
-  "model_name_or_path": "meta-llama/Meta-Llama-3.1-70B",
   "output_dir": "./checkpoints/helion-2.5-rnd",
   "overwrite_output_dir": true,
   "do_train": true,
@@ -102,33 +102,6 @@
   "ddp_find_unused_parameters": false,
   "ddp_bucket_cap_mb": null,
   "ddp_broadcast_buffers": null,
-  "dataset_config": {
-    "scientific_papers": {
-      "path": "arxiv_papers",
-      "split": "train",
-      "weight": 0.25
-    },
-    "code_repositories": {
-      "path": "the-stack-dedup",
-      "split": "train",
-      "weight": 0.25
-    },
-    "mathematical_proofs": {
-      "path": "math_qa",
-      "split": "train",
-      "weight": 0.15
-    },
-    "conversational_data": {
-      "path": "sharegpt",
-      "split": "train",
-      "weight": 0.20
-    },
-    "multilingual_corpus": {
-      "path": "mc4",
-      "split": "train",
-      "weight": 0.15
-    }
-  },
   "data_preprocessing": {
     "max_seq_length": 131072,
     "truncation": true,

 {
+  "model_name_or_path": "DeepXR/Helion-2.5-Rnd",
   "output_dir": "./checkpoints/helion-2.5-rnd",
   "overwrite_output_dir": true,
   "do_train": true,
   "ddp_find_unused_parameters": false,
   "ddp_bucket_cap_mb": null,
   "ddp_broadcast_buffers": null,
   "data_preprocessing": {
     "max_seq_length": 131072,
     "truncation": true,