HyperNova 60B 2605

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

Table of Contents

• Highlights
• Model Overview
• Key Characteristics
• Quick Start
• What's New in HyperNova 60B 2605
• Tool Calling
• Training & Fine-Tuning
• Architecture
• Evaluation & Benchmarks
• Languages
• Intended Use
• Safety & Limitations
• Model Information
• 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.

Key Characteristics

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:

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

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

Architecture

Model Specifications

Evaluation

Benchmarks Results

HyperNova 60B Benchmark Comparison

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

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):

Performance evaluation conditions

Our performance evaluation follows the spirit of Artificial Analysis.

• 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

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 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

Citation

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

@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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