Instructions to use MultiverseComputingCAI/Hypernova-60B-2605 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultiverseComputingCAI/Hypernova-60B-2605 with Transformers:

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

pipe = pipeline("text-generation", model="MultiverseComputingCAI/Hypernova-60B-2605")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiverseComputingCAI/Hypernova-60B-2605")
model = AutoModelForCausalLM.from_pretrained("MultiverseComputingCAI/Hypernova-60B-2605")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MultiverseComputingCAI/Hypernova-60B-2605 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MultiverseComputingCAI/Hypernova-60B-2605"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultiverseComputingCAI/Hypernova-60B-2605",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MultiverseComputingCAI/Hypernova-60B-2605

SGLang

How to use MultiverseComputingCAI/Hypernova-60B-2605 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 "MultiverseComputingCAI/Hypernova-60B-2605" \
    --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": "MultiverseComputingCAI/Hypernova-60B-2605",
		"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 "MultiverseComputingCAI/Hypernova-60B-2605" \
        --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": "MultiverseComputingCAI/Hypernova-60B-2605",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MultiverseComputingCAI/Hypernova-60B-2605 with Docker Model Runner:
```
docker model run hf.co/MultiverseComputingCAI/Hypernova-60B-2605
```

arturo-fredes commited on Jun 5

Commit

39bbf84

verified ·

1 Parent(s): 0f11bcf

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@@ -338,10 +338,11 @@ The model was trained primarily on English-language data. Performance on other l
 - Use human oversight for critical applications
 - Perform task-specific evaluation prior to deployment
 <div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
 <table style="width:100%;border-collapse:collapse;font-size:13px">
 <thead><tr>
-<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FF053F;color:#FF053F"></th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;">GPT-OSS-120B<span style="visibility:hidden"> Gemma4-31B</span></th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;">HyperNova 60B 2605<span style="visibility:hidden"> Qwen3.6-35BA3B</span></th><th style="padding:10px 7px;text-align:left;font-weight:500;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;">Comment</th></tr></thead>
 <tbody>
 <tr>
 <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StereoSet <sub><small>stereotype score</small></sub></td>
@@ -395,6 +396,7 @@ The model was trained primarily on English-language data. Performance on other l
 </table>
 </div>
 ---
 ## Model Information

 - Use human oversight for critical applications
 - Perform task-specific evaluation prior to deployment
 <div style="font-family:-apple-system,BlinkMacSystemFont,'Segoe UI',Roboto,sans-serif;max-width:1000px;margin:0 auto;padding:16px 0">
 <table style="width:100%;border-collapse:collapse;font-size:13px">
 <thead><tr>
+<th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FF053F;color:#FF053F;vertical-align:bottom;line-height:1.3">Metric</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;vertical-align:bottom;line-height:1.3">GPT-OSS<br>120B</th><th style="padding:10px 7px;text-align:center;font-weight:500;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;vertical-align:bottom;line-height:1.3">HyperNova 60B<br>2605</th><th style="padding:10px 7px;text-align:left;font-weight:600;border-bottom:2px solid #FF053F;color:#FF053F;font-size:14px;vertical-align:bottom;line-height:1.3">Comment</th></tr></thead>
 <tbody>
 <tr>
 <td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StereoSet <sub><small>stereotype score</small></sub></td>
 </table>
 </div>
 ---
 ## Model Information