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", 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=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
```

Hypernova-60B-2605

File size: 24,732 Bytes

---
base_model:
- openai/gpt-oss-120b
- MultiverseComputingCAI/HyperNova-60B
library_name: transformers
license: apache-2.0
---
<div align="center">

# HyperNova 60B 2605

### Powered by CompactifAI

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![HuggingFace](https://img.shields.io/badge/🤗-Model_Hub-yellow.svg)](https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2605)
[![Discord](https://img.shields.io/badge/Discord-Community-5865F2?logo=discord&logoColor=white)](https://discord.gg/cGas9uStqp)

**Optimized for Efficient Inference** · **Reduced Memory Footprint** · **Native Tool Calling Support**

</div>

---

## Table of Contents

- [Highlights](#highlights)
- [Model Overview](#model-overview)
- [Key Characteristics](#key-characteristics)
- [Quick Start](#quick-start)
- [What's New in HyperNova 60B 2605](#whats-new-in-hypernova-60b-2605)
- [Tool Calling](#tool-calling)
- [Training & Fine-Tuning](#training--fine-tuning)
- [Architecture](#architecture)
- [Evaluation & Benchmarks](#evaluation--benchmarks)
- [Languages](#languages)
- [Intended Use](#intended-use)
- [Safety & Limitations](#safety--limitations)
- [Model Information](#model-information)
- [Citation](#citation)

---

## Model Overview

**HyperNova 60B 2605**, developed by [**Multiverse Computing**](https://multiversecomputing.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=hypernova_60b_2605_overview), is an open-weight model designed for powerful **general** reasoning, **coding**, and versatile developer use.

The model is **instruction-tuned** and supports **native tool calling** (function calling with defined schemas, structured outputs, and agent-style workflows). HyperNova 60B 2605 is intended for code generation, RAG, and tool-augmented applications.

## Technical Deep Dive
For a detailed explanation of the compression architecture, model compression process, and benchmark results behind Hypernova-60B, read [this full technical article by Johanna Angulo, Evaluation Manager at Multiverse Computing.](https://multiversecomputing.com/papers/hypernova-60b-2602-same-intelligence-half-the-size-improved-tool-calling-capability?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=hypernova_60b_2605_article)

---

## Key Characteristics

| Characteristic        | Description |
|-----------------------|-------------|
| 🛠️ **Tool calling**  | Native support; OpenAI-style function / tool calling schemas; suited to coding agents and structured outputs |
| 🧠 **Parameters**     | 60B total parameters |
| 📐 **Architecture**   | Decoder-only Transformer |
| Primary language      | English |
| Other languages       | Not formally evaluated |
---
## Quick Start
This model can be loaded with the **Transformers** API. Use `trust_remote_code=True` (required for the gpt-oss architecture). Recommended approach: `AutoModelForCausalLM` with `apply_chat_template`:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "MultiverseComputingCAI/HyperNova-60B-2605"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto",
    trust_remote_code=True,
)
messages = [{"role": "user", "content": "What is a Hypernova?"}]
inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True,
)
inputs = inputs.to(model.device)
attention_mask = torch.ones_like(inputs, dtype=torch.long, device=inputs.device)
outputs = model.generate(
    inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    attention_mask=attention_mask,
)
reply = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(reply)
```
Alternatively you can use the `pipeline` API with `trust_remote_code=True`; the pipeline returns the full conversation structure, so extract the assistant message from `outputs[0]["generated_text"]` as needed.

---

## What’s New in HyperNova 60B 2605

**HyperNova 60B 2605** is an improved version of **HyperNova 60B 2602**, with this release focused on **coding** and **general** capability backed by higher scores on several benchmarks.

### Summary

- **Improvement focus vs HyperNova 60B 2602:** stronger **coding** (coding-style tasks) and **general** benchmark performance.
- **Tool use:** Retains native support for function calling, structured outputs, and agent-style workflows (OpenAI-style schemas).
- **Reasoning:** Compatible with configurable reasoning effort (e.g. low / medium / high in system prompt) where the format is preserved; full chain-of-thought available for debugging and analysis.
- **Evaluated** on coding and tool-heavy benchmarks (e.g. Tau2-bench, Terminal-Bench) alongside **general** intelligence benchmarks.

---

## Tool Calling

HyperNova 60B 2605 supports **native tool use** and is well-suited for:

- **Function calling** with defined schemas  
- **Structured outputs**  
- **Coding-oriented tool workflows** (e.g. browser tasks, code execution where supported)

The model can detect when to invoke tools, emit structured JSON tool calls, and consume tool outputs to continue generation. Tool-calling behavior follows **OpenAI-style schemas**; compatibility refers to format and structure—exact parity with the base or other models is not guaranteed.
Compared with HyperNova 60B 2602, this release improves on **coding** and **general** evaluation tracks—including IFBench, Tau2-bench, Terminal Bench, and AA-LCR under the high-reasoning setup reported below.

### Example Tool Call

```json
{
  "name": "get_weather",
  "arguments": {
    "city": "Paris",
    "date": "2026-02-10"
  }
}
```

---

## Architecture

### Model Specifications

| Specification     | Value              |
|-------------------|--------------------|
| Total parameters  | 60B, 4.8B active MoE |

---

## Evaluation 

### Benchmarks Results

# HyperNova 60B Benchmark Comparison

<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 2602<span style="visibility:hidden"> Gemma4-26BA4B</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></tr></thead>
<tbody>
<tr><td colspan="4" style="padding:8px 12px;font-weight:600;color:#FF053F;border-bottom:1px solid rgba(255, 5, 63, 0.2);background:rgba(255, 5, 63, 0.1)">Knowledge &amp; Reasoning</td></tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">HLE</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">18.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">7.3</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.0</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">MMLU-Pro</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">79.6</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.3</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">76.8</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AIME25</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">93.7</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">86.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">90.0</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">GPQA:d</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">74.6</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">65.6</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">71.9</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">IFBench</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">67.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">59.4</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">66.6</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AA-LCR</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">49.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">35.7</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">40.3</td>
</tr>
<tr><td colspan="4" style="padding:8px 12px;font-weight:600;color:#FF053F;border-bottom:1px solid rgba(255, 5, 63, 0.2);background:rgba(255, 5, 63, 0.1)">Agent &amp; Tool Use</td></tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Tau2-bench <sub><small>Telecom</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">63.7</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">60.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">61.7</td>
</tr>
<tr><td colspan="4" style="padding:8px 12px;font-weight:600;color:#FF053F;border-bottom:1px solid rgba(255, 5, 63, 0.2);background:rgba(255, 5, 63, 0.1)">Coding</td></tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">SciCode</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">41.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">33.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">36.0</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">LiveCodeBench</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">62.8</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">51.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">68.7</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Terminal Bench</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">24.2</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">12.1</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">15.9</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">AIDER</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">43.6</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">26.2</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">34.2</td>
</tr>
</tbody>
</table>
</div>


![Benchmarks](assets/benchmarks.png)

![CodingBenchmarks](assets/coding_benchmarks.png)

### Evaluation Methodology

Benchmark scores were obtained with the following setups. Methodology varies by benchmark family.

#### Inference:
- **Backend:** VLLM 0.13.0
- **Decoding:**  `temp 1.0`, `top_p 1.0`
- **Reasoning Effort:** high

| Benchmark | Framework | Repeats | Other |
|-----------|-----------|---------|-------|
| HLE | NeMo-Skills | 1 | Judge: `openai/gpt-4o` |
| MMLU-Pro | NeMo-Skills | 1 | |
| AIME25 | NeMo-Skills | 10 | |
| GPQA:d | NeMo-Skills | 5 ||
| LiveCodeBench | NeMo-Skills | 3 | Split: `test_v5_2407_2412` (Jul–Dec 2024) |
| IFBench | NeMo-Skills | 5 ||
| AA-LCR | NeMo-Skills | 3 |Judge: `Qwen/Qwen3-235B-A22B-Instruct-2507` (judge temp 0.7, top_p 0.8).|
| SciCode | NeMo-Skills | 3 ||
| 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` . Metric: refusal_rate (low on safe, high on unsafe). |

### Inference Performance

#### 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 |
|--------|-------------:|-------------------:|
| Concurrency | 128 | 128 |
| Throughput (tok/s) | 3,821 | 5,210 ||
| TTFT (s) | 7.04 | 4.85 |
| Model weights (GB) | 65 | 32 |


#### 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** 

![Performance](assets/performance.png)

---

## Languages

- **Primary language**: English  
- **Other languages**: Not formally evaluated  

The model was trained primarily on English-language data. Performance on other languages may vary and has not been systematically measured.

---

## Intended Use

### Recommended Use Cases

- **Reasoning and analysis** (with configurable reasoning effort where supported)  
- **Tool-augmented applications**, with emphasis on **coding** and **general** assistant use (function calling, web browsing, code execution, structured outputs)  
- **Code generation and reasoning**  
- **Chatbots and virtual assistants**  
- **Retrieval-augmented generation (RAG)**  

### Out-of-Scope Uses

- Harmful, illegal, or deceptive content generation  
- Impersonation of real individuals without consent  
- High-risk decision-making without human oversight  
- Surveillance or tracking of individuals  
- Any use that violates applicable laws or regulations  

---

## Safety & Limitations

### Known Limitations

- **English-centric** training data.  
- **Format:** For best results, use the same [harmony response format](https://huggingface.co/openai/gpt-oss-120b) as gpt-oss-120b where applicable; behavior may differ otherwise.  
- **Tool calling** depends on correct schema and tool design; exact parity with gpt-oss-120b or other models is not guaranteed.  

### Recommendations

- Validate tool outputs before execution  
- 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>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">55.0</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">56.0</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Share of examples where the model prefers a stereotypical completion. Lower is better.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StereoSet <sub><small>language model score</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.9</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">97.3</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Share of examples where the model assigns higher probability to a meaningful vs. meaningless completion. Higher is better.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StereoSet <sub><small>ICAT</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">87.3</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">85.6</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Idealized CAT score combining language-model quality and stereotype bias. Higher is better.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StrongREJECT <sub><small>jailbreak rate</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">0.003</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">0</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Fraction of adversarial jailbreak prompts that elicit a harmful response. Lower is better; 0 is best.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">StrongREJECT <sub><small>metric</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">0.013</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">0</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Overall harmful-response rate on the StrongREJECT benchmark. Lower is better; 0 is best.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">XSTest <sub><small>safe refusal</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">29.6</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">30.4</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Rate at which benign prompts are incorrectly refused (over-refusal). Lower is better.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">XSTest <sub><small>unsafe refusal</small></sub></td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">99.5</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">99.0</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Rate at which harmful prompts are correctly refused. Higher is better.</td>
</tr>
<tr>
<td style="padding:7px 7px;padding-left:20px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">BBQ</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.9</td>
<td style="padding:7px 7px;text-align:center;border-bottom:1px solid rgba(128, 128, 128, 0.15)">96.4</td>
<td style="padding:7px 7px;border-bottom:1px solid rgba(128, 128, 128, 0.15);">Accuracy on ambiguous QA pairs designed to surface social bias. Higher is better.</td>
</tr>
</tbody>
</table>
</div>


---

## Model Information

| Field         | Value               |
|--------------|--------------------- |
| Model name   | HyperNova 60B 2605   |
| Version      | 2605                 |
| Release date | 26/02/2026           |
| Developed by | [Multiverse Computing](https://multiversecomputing.com/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_presence_0626&utm_content=hypernova_60b_2605_modelinfo) |
| License      | Apache 2.0           |
| Contact      | business@multiversecomputing.com   |

---

## Citation

If you use this model, please cite the base model and this variant:

```bibtex
@misc{openai2025gptoss120b,
  title         = {gpt-oss-120b \& gpt-oss-20b Model Card},
  author        = {OpenAI},
  year          = {2025},
  eprint        = {2508.10925},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CL},
  url           = {https://arxiv.org/abs/2508.10925}
}
@misc{hypernova60b2605,
  title = {HyperNova 60B 2605: Model developed based on gpt-oss-120b},
  author = {Multiverse Computing},
  year = {2026},
  url = {https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2605},
  note = {Model developed based on openai/gpt-oss-120b using CompactifAI technology}
}
@misc{ryskulov2026efficientknowledgedistillationllms,
      title={Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss}, 
      author={Bakbergen Ryskulov and Iker García-Ferrero and David Montero and David Jansen and Ali Hashemi and Jezabel R. Garcia and Antonio Tiene and Román Orús},
      year={2026},
      eprint={2608.03796},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2608.03796}, 
}
```

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