--- base_model: - openai/gpt-oss-120b - MultiverseComputingCAI/HyperNova-60B library_name: transformers license: apache-2.0 ---
# HyperNova 60B 2602 ### 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-2602) [![Discord](https://img.shields.io/badge/Discord-Community-5865F2?logo=discord&logoColor=white)](https://discord.gg/8mT9FveN) **Optimized for Efficient Inference** · **Reduced Memory Footprint** · **Native Tool Calling Support**
--- ## 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} } ``` **Built by [Multiverse Computing](https://www.multiversecomputing.com)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2602/discussions) · [Discord](https://discord.gg/8mT9FveN)