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

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 19 days ago

Commit

b96e111

verified ·

1 Parent(s): 57c7100

Update README.md

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README.md +14 -2

README.md CHANGED Viewed

@@ -156,7 +156,7 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 - **Inference library**: vLLM 0.13.0
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
 - **Reasoning effort**: high
-- **Decoding**: temperature = 0.6, max_tokens = 131072, top_p = 1.0, top_k = 0
 - **Batch size**: 64
 #### IFBench, AA-LCR, SciCode
@@ -165,7 +165,7 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 - **Inference library**: vLLM 0.13.0
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
 - **Reasoning effort**: high
-- **Decoding**: temperature = 1.0, max_tokens = 131072, top_p = 1.0, top_k = 0
 - **Batch size**: 64
 #### Tau2-bench (Telecom)
@@ -188,6 +188,17 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 - **Reproducibility**: subset from AA (https://artificialanalysis.ai/methodology/intelligence-benchmarking#terminal-bench-hard)
 - **Agent**: terminus-2, max episodes 100; repeats 3;
 ### Quantitative Results (Reported & Planned)
@@ -203,6 +214,7 @@ Benchmark scores were obtained with the following setups. Methodology varies by
 | LiveCodeBench         | 62.75                         | 51.53                       | 68.68                    |
 | Terminal Bench        | 24.24                         | 12.12                       | 15.91                    |
 | AA-LCR                | 49.00                         | 35.67                       | 40.33                    |
 ![Benchmarks](assets/benchmarks.png)

 - **Inference library**: vLLM 0.13.0
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
 - **Reasoning effort**: high
+- **Decoding**: temperature = 1.0, top_p = 1.0
 - **Batch size**: 64
 #### IFBench, AA-LCR, SciCode
 - **Inference library**: vLLM 0.13.0
 - **Hardware**: 1× NVIDIA H200 Tensor Core GPU
 - **Reasoning effort**: high
+- **Decoding**: temperature = 1.0,top_p = 1.0
 - **Batch size**: 64
 #### Tau2-bench (Telecom)
 - **Reproducibility**: subset from AA (https://artificialanalysis.ai/methodology/intelligence-benchmarking#terminal-bench-hard)
 - **Agent**: terminus-2, max episodes 100; repeats 3;
+#### Aider polyglot
+- **Evaluation framework**: [Aider-AI/aider](https://github.com/Aider-AI/aider)
+- **Hardware**: 2× NVIDIA H200 Tensor Core GPU (host with Docker)
+- **Dataset**: `polyglot-benchmark` (225 exercises across multiple languages)
+- **Reasoning effort**: high (passed via `--reasoning-effort`)
+- **Decoding**: temperature = 1.0, top_p = 1.0 (configurable via `generation_config` / `--read-model-settings` YAML)
+- **Edit format**: `whole` (also supports `diff | udiff | diff-fenced | architect`)
+- **Reproducibility**: leaderboard-aligned; `--tries=2` (repeats)
 ### Quantitative Results (Reported & Planned)
 | LiveCodeBench         | 62.75                         | 51.53                       | 68.68                    |
 | Terminal Bench        | 24.24                         | 12.12                       | 15.91                    |
 | AA-LCR                | 49.00                         | 35.67                       | 40.33                    |
+| AIDER                 | 43.60                         | 26.2                        | 34.2                     |
 ![Benchmarks](assets/benchmarks.png)