README.md

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

# HyperNova 60B 2602

### Powered by CompactifAI

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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 2602](#whats-new-in-hypernova-60b-2602)
- [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 2602** is a **model developed based on [OpenAI’s gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b)**, developed by **Multiverse Computing**. The original gpt-oss-120b is an open-weight model (117B parameters, 5.1B active in MoE) designed for powerful reasoning, agentic tasks, and versatile developer use. This version is compressed with **CompactifAI**, Multiverse Computing’s proprietary technology, reducing parameter count and memory requirements while aiming to preserve strong reasoning. 

The model is **instruction-tuned** and supports **native tool calling** (function calling with defined schemas, structured outputs, and agent-style workflows). HyperNova 60B 2602 is intended for the same broad use cases as gpt-oss-120b—reasoning, code generation, RAG, and tool-augmented applications—with **lower memory footprint** and deployment flexibility.

---

## Key Characteristics

| Characteristic        | Description |
|-----------------------|-------------|
| Base model            | [OpenAI gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) (117B params, MoE; open-weight, Apache 2.0) |
| 🛠️ **Tool calling**  | Native support; OpenAI-style function / tool calling schemas; agentic use (e.g. function calling, structured outputs) |
| 🧠 **Parameters**     | 60B total parameters after CompactifAI compression (reduced vs. base 117B) |
| 📐 **Architecture**   | Decoder-only Transformer (from gpt-oss lineage) |
| 🗜️ **Compression**   | CompactifAI (proprietary compression technology) |
| 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-2602"
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 2602

**HyperNova 60B 2602** is a model developed based on **gpt-oss-120b**, retaining the base model’s strengths while reducing memory and improving deployment flexibility.

### Summary

- **Model developed based on [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b):** Same Apache 2.0 license and design goals (reasoning, agentic tasks, tool use); smaller footprint via CompactifAI.
- **Tool use:** Retains 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 tool-focused benchmarks (e.g. BFCL v4, Tau2-bench) and general benchmarks alongside other CompactifAI and gpt-oss variants.

---

## Tool Calling

HyperNova 60B 2602 supports **native tool use** and is well-suited for:

- **Function calling** with defined schemas  
- **Structured outputs**  
- **Agentic operations** (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.

### Example Tool Call

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

---

## Training & Fine-Tuning

### Base Model: gpt-oss-120b

The base model [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) was trained on OpenAI’s **harmony response format** and is intended for use with that format for correct behavior. It supports configurable reasoning levels (low / medium / high) and native tool use. See the [original model card](https://huggingface.co/openai/gpt-oss-120b) and [arXiv:2508.10925](https://arxiv.org/abs/2508.10925) for details.

### CompactifAI Compression & Optional Fine-Tuning

- **Compression:** CompactifAI was applied to produce a smaller, efficient model (60B parameters) while aiming to preserve reasoning and tool-use capabilities.
- **Optional fine-tuning:** This variant may include additional fine-tuning for tool calling and structured outputs; exact training details are model-specific.

---

## Architecture

### Model Specifications

| Specification     | Value              |
|-------------------|--------------------|
| Base model        | [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) (117B params, 5.1B active MoE) |
| Total parameters  | 60B, 4.8B active MoE |

---

## Evaluation & Benchmarks

### Evaluation Methodology

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

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

- **Evaluation framework**: [Lighteval](https://github.com/huggingface/lighteval) 
- **Inference library**: vLLM 0.14.0  
- **Reasoning effort**: medium  
- **Decoding**: temperature = 0.6, max_tokens = 131072, top_p = 1.0, top_k = 0  
- **Batch size**: 64  

#### IFBench, AA-LCR, SciCode

- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills) 
- **Inference library**: vLLM 0.14.0  
- **Reasoning effort**: medium  
- **Decoding**: temperature = 1.0, max_tokens = 131072, top_p = 1.0, top_k = 0  
- **Batch size**: 64
  
#### BFCL v4 (17 splits)

- **Evaluation framework**: [EvalScope](https://github.com/EvalScope/EvalScope) 1.4.1
- **Inference library**: vLLM 0.14.0  
- **Reasoning effort**: high  
- **Decoding**: temperature = 0.6, max_tokens = 16384, parallel_tool_calls = true, tool-call parser openai

#### Tau2-bench (Telecom)

- **Evaluation framework**: [EvalScope](https://github.com/EvalScope/EvalScope) 1.4.1
- **Inference library**: vLLM 0.14.0  
- **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 == 0.15.0
- **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;

### Quantitative Results (Reported & Planned)

Scores are accuracy or benchmark-specific metrics. Use `—` or *TBD* for evaluations not yet run. Reported numbers use the methodology described above (reasoning: cai-eval + Nemo-skills; BFCL v4 and Tau2-bench: cai-eval + EvalScope); other entries to be documented.

| Benchmark             | gpt-oss-20b           | gpt-oss-120b           | HyperNova 60B 2602       |
|-----------------------|-----------------------|------------------------|--------------------------|
| MMLU-Pro              | 74                    | 78                     | 74                       |
| BFCL v4               | 61                    | 64                     | 62                       |
| Tau2-bench (Telecom)  | 59                    | 68                     | 61                       |
| AIME25                | 72                    | 80                     | 76                       |
| GPQA:d                | 63                    | 69                     | 69                       |
| IFBench               | 55                    | 63                     | 60                       |
| SciCode               | 34                    | 38                     | 32                       |
| LiveCodeBench         | 64                    | 66                     | 64                       |
| Terminal Bench        | 9                     | 22                     | 16                       |
| AA-LCR                | 37                    | 50                     | 36                       |
| AA-Omnis. Index       | -40                   | -36                    | -41                      |
| AA-Omnis. Accuracy    | 16                    | 21                     | 15                       |

![Intelligence](assets/intelligence.png)
![Tool-calling](assets/tool-calling.png)

### Quantitative Results (Inference Performance)

Representative throughput and memory under the evaluation setup above. Comparison against **gpt-oss-20b** and **gpt-oss-120b** on the same hardware.

#### Performance evaluation conditions

Describe the setup used to obtain the numbers in the table below (replace the placeholders or add a short paragraph):

- **Inference library**: vLLM 0.14.0
- **Hardware**: 4× NVIDIA H200 Tensor Core GPU
- **Conditions**: batch size=512, context length=512, decode length=256
- **Notes**: dtype=default

| Metric                     | gpt-oss-20b              | gpt-oss-120b             | HyperNova 60B 2602       | Hardware                      |
|----------------------------|--------------------------|--------------------------|--------------------------|-------------------------------|
| Tokens / second (decode)   | 250                      | 228                      | 240                      | 4× NVIDIA H200 Tensor Core GPU|
| Time to first token (ms)   | 26                       | 26                       | 25                       | 4× NVIDIA H200 Tensor Core GPU|
| Peak GPU memory (GB)       | 13                       | 61                       | 32                       | 4× NVIDIA H200 Tensor Core GPU|

![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

Aligned with [gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) use cases, with the benefit of a smaller footprint:

- **Reasoning and analysis** (with configurable reasoning effort where supported)  
- **Tool-augmented and agentic applications** (function calling, web browsing, code execution, structured outputs)  
- **Code generation and reasoning**  
- **Chatbots and virtual assistants**  
- **Retrieval-augmented generation (RAG)**  
- **Deployments** where gpt-oss-120b is desirable but memory or latency is constrained  

### 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 (inherited from base model).  
- **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.  
- **Compression** may affect some behaviors; evaluate for your use case.  

### 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 2602   |
| Based on     | [openai/gpt-oss-120b](https://huggingface.co/openai/gpt-oss-120b) |
| Version      | 2602                 |
| 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{hypernova60b2602,
  title = {HyperNova 60B 2602: Model developed based on gpt-oss-120b},
  author = {Multiverse Computing},
  year = {2026},
  url = {https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2602},
  note = {Model developed based on openai/gpt-oss-120b using CompactifAI technology}
}
```

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