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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultiverseComputingCAI/Hypernova-60B-2605")
model = AutoModelForCausalLM.from_pretrained("MultiverseComputingCAI/Hypernova-60B-2605", device_map="auto")
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=256)
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

46b7b12

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@@ -304,7 +304,10 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 | Tau2-bench (Telecom) | EvalScope 1.4.1 | 3 |Judge / user simulator: temperature 0.7, timeout 600. Subset: telecom (default). Max steps: 100. Tool-call parser: openai (agent), hermes (judge). |
 | Terminal-Bench Hard | laude-institute/harbor 0.1.43 | 3 |  max-model-len 131072. Subset: [Artificial Analysis](https://artificialanalysis.ai/methodology/intelligence-benchmarking#terminal-bench-hard). Agent: terminus-2. Max episodes: 100 |
 | Aider polyglot | Aider-AI/aider | 2 |Dataset: polyglot-benchmark (225 exercises). Edit format: whole. Leaderboard-aligned; `--tries=2`. |
 ### Inference Performance

 | Tau2-bench (Telecom) | EvalScope 1.4.1 | 3 |Judge / user simulator: temperature 0.7, timeout 600. Subset: telecom (default). Max steps: 100. Tool-call parser: openai (agent), hermes (judge). |
 | Terminal-Bench Hard | laude-institute/harbor 0.1.43 | 3 |  max-model-len 131072. Subset: [Artificial Analysis](https://artificialanalysis.ai/methodology/intelligence-benchmarking#terminal-bench-hard). Agent: terminus-2. Max episodes: 100 |
 | Aider polyglot | Aider-AI/aider | 2 |Dataset: polyglot-benchmark (225 exercises). Edit format: whole. Leaderboard-aligned; `--tries=2`. |
+| StereoSet | inspect-ai 0.3.205 + inspect_evals 0.3.106 | 1 | Multiple-choice / logprob; no external judge. Dataset: 2,115 examples (gender, profession, race, religion). Metrics: stereotype_score (50 = ideal), language_model_score, ICAT. |
+| BBQ | inspect-ai 0.3.205 + inspect_evals 0.3.106 | 1 | Multiple-choice; no external judge. Full dataset: 58,492 MCQ across 11 bias dimensions. Metric: accuracy. |
+| StrongREJECT | inspect-ai 0.3.205 + inspect_evals 0.3.106 | 1 | Dataset: 313 forbidden prompts. Judge: `openrouter/openai/gpt-4o`. Metrics: jailbreak_rate, strong_reject_metric (0.0 = ideal). `max_retries`: 3. |
+| XSTest | inspect-ai 0.3.205 + inspect_evals 0.3.106 | 1 | Dataset: safe (250) + unsafe (200); one subset per run. Judge: `openai/gpt-4o` (default) or `openrouter/openai/gpt-4o`. Metric: refusal_rate (low on safe, high on unsafe). |
 ### Inference Performance