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 5 days ago

Commit

efb7e53

verified ·

1 Parent(s): 01ee45f

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@@ -225,13 +225,9 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 #### Metrics reported
 - **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
-- **End-to-End Latency per Query (lower is better):** Median end-to-end response time for each query from the time the query is sent.
-- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
 - **Time to first token (TTFT) (lower is better):** Median time to first token.
-- **Estimated total memory — (lower is better):** Median from each GuideLLM phase (estimated total footprint: weights plus KV contribution from monitored usage).
 - **Model weights (lower is better):**
-On the same hardware and harness, **HyperNova 60B 2605** is compared to **gpt-oss-120b** using GuideLLM. Each table lists **median** values for that model at each **concurrency phase** (1 → 256 concurrent requests).
 | Metric | GPT-OSS-120B | Hypernova 60B 2605 |
 |--------|-------------:|-------------------:|
@@ -244,18 +240,17 @@ On the same hardware and harness, **HyperNova 60B 2605** is compared to **gpt-os
 | Model weights (GB) | 121.54 | 31.81 |
 #### Performance evaluation conditions
 Our performance evaluation follows the spirit of [Artificial Analysis](https://artificialanalysis.ai/methodology/system-load-test).
-- **Inference library**: vLLM 0.13.0
 - **Monitoring libraries**: GuideLLM, nvidia-ml-py
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
-- **Conditions**: **concurrency phases** 1, 2, 4, 8, 16, 32, 64, 128, 192, and 256 concurrent requests (one GuideLLM phase each)
 - **Phase duration**: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
-- **Workload shape:** input length is ~1000 tokens per query (median); median output length varies by phase and model.
-- **Streaming**: Benchmarking is conducted with streaming enabled.
 The figure below is a **side-by-side comparison at concurrency = 128 only**

 #### Metrics reported
 - **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
 - **Time to first token (TTFT) (lower is better):** Median time to first token.
 - **Model weights (lower is better):**
 | Metric | GPT-OSS-120B | Hypernova 60B 2605 |
 |--------|-------------:|-------------------:|
 | Model weights (GB) | 121.54 | 31.81 |
 #### Performance evaluation conditions
 Our performance evaluation follows the spirit of [Artificial Analysis](https://artificialanalysis.ai/methodology/system-load-test).
+- **Inference library**: vLLM 0.18.0
 - **Monitoring libraries**: GuideLLM, nvidia-ml-py
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **Conditions**: **concurrency phases** 128
 - **Phase duration**: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
+- **Workload shape:** 1k input / 1k output
+- **Decode:** temperature: 0.0, top_p: 1.0
 The figure below is a **side-by-side comparison at concurrency = 128 only**