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.
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:
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.
If you use this model, please cite the base model and this variant:
bibtex
1@misc{openai2025gptoss120b,
2 title = {gpt-oss-120b \& gpt-oss-20b Model Card},
3 author = {OpenAI},
4 year = {2025},
5 eprint = {2508.10925},
6 archivePrefix = {arXiv},
7 primaryClass = {cs.CL},
8 url = {https://arxiv.org/abs/2508.10925}
9}
10@misc{hypernova60b2605,
11 title = {HyperNova 60B 2605: Model developed based on gpt-oss-120b},
12 author = {Multiverse Computing},
13 year = {2026},
14 url = {https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2605},
15 note = {Model developed based on openai/gpt-oss-120b using CompactifAI technology}
16}
17@misc{ryskulov2026efficientknowledgedistillationllms,
18 title={Efficient Knowledge Distillation for LLMs: Offline Top-K Logits and a Fused Chunked KL Loss},
19 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},
20 year={2026},
21 eprint={2608.03796},
22 archivePrefix={arXiv},
23 primaryClass={cs.CL},
24 url={https://arxiv.org/abs/2608.03796},
25}
