README.md

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

# HyperNova 60B 2605

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**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**, 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)

---

## 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

### Evaluation Methodology

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

#### HLE, MMLU-Pro, AIME25, GPQA:d, LiveCodeBench

- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills)
- **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

- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills) 
- **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)

- **Evaluation framework**: [EvalScope](https://github.com/EvalScope/EvalScope) 1.4.1
- **Inference library**: vLLM 0.13.0  
- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
- **Reasoning effort**: high (agent `extra_body.reasoning_effort`)  
- **Decoding (agent)**: temperature = 1.0, top_p = 1.0, min_tokens = 1  
- **Decoding (judge / user simulator)**: temperature = 0.7, timeout = 600  
- **Reproducibility**: subset telecom (default); max steps 100; repeats 3; tool-call parser openai (agent), hermes (judge)  

#### Terminal-Bench Hard (Artificial Analysis subset):

- **Evaluation framework**: laude-institute/harbor == 0.1.43
- **Inference library**: vLLM == 0.13.0
- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
- **Reasoning effort**: high
- **Decoding**: temperature = 1.0, top_p = 1.0, max-model-len = 131072
- **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)


| Benchmark             | gpt-oss-120b                  | HyperNova 60B 2602          | HyperNova 60B 2605       |
|-----------------------|-------------------------------|-----------------------------|--------------------------|
| HLE                   | 18.50                         | 7.28                        | 14.97                    |
| MMLU-Pro              | 79.64                         | 74.25                       | 76.77                    |
| Tau2-bench (Telecom)  | 63.74                         | 60.53                       | 61.70                    |
| AIME25                | 93.67                         | 86.00                       | 90.00                    |
| GPQA:d                | 74.64                         | 65.56                       | 71.92                    |
| IFBench               | 67.01                         | 59.40                       | 66.57                    |
| SciCode               | 41.52                         | 33.53                       | 36.00                    |
| 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)

![LiveCodeBench](assets/livecodebench.png)

### Quantitative Results (Inference Performance)

#### 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 |
|--------|-------------:|-------------------:|
| Concurrency | 128 | 128 |
| Throughput (tok/s) | 3,821 | 5,210 |
| E2E latency (s) | 24.05 | 14.74 |
| Output speed (tok/s) | 57.79 | 69.31 |
| TTFT (s) | 7.04 | 4.85 |
| Est. total memory (GB) | 123.55 | 38.83 |
| 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** 

![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  

---

## Model Information

| Field         | Value               |
|--------------|--------------------- |
| Model name   | HyperNova 60B 2605   |
| Version      | 2605                 |
| Release date | 26/02/2026           |
| Developed by | Multiverse Computing |
| 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}
}
```

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