Hypernova-60B-2605 API & Inference Endpoint

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

Table with columns: Characteristic, Description
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

Table with columns: Specification, Value
Specification	Value
Total parameters	60B, 4.8B active MoE

Evaluation

Benchmarks Results

HyperNova 60B Benchmark Comparison

Benchmarks

CodingBenchmarks

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

Table with columns: Benchmark, Framework, Repeats, Other
Benchmark	Framework	Repeats	Other
HLE	NeMo-Skills	1	Judge: `openai/gpt-4o`
MMLU-Pro	NeMo-Skills	1
AIME25	NeMo-Skills	10
GPQA:d	NeMo-Skills	5

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):

Table with columns: Metric, GPT-OSS-120B, Hypernova 60B 2605
Metric	GPT-OSS-120B	Hypernova 60B 2605
Concurrency	128	128
Throughput (tok/s)	3,821	5,210
TTFT (s)	7.04	4.85
Model weights (GB)	65	32

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

Performance

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

Table with columns: Field, Value
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}
}

Built by Multiverse Computing · Report an issue · Discord

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.

Technical Deep Dive

Key Characteristics

Table with columns: Characteristic, Description
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)

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)

Example Tool Call

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

Architecture

Model Specifications

Table with columns: Specification, Value
Specification	Value
Total parameters	60B, 4.8B active MoE

Evaluation

Benchmarks Results

HyperNova 60B Benchmark Comparison

Benchmarks

CodingBenchmarks

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

Table with columns: Benchmark, Framework, Repeats, Other
Benchmark	Framework	Repeats	Other
HLE	NeMo-Skills	1	Judge: `openai/gpt-4o`
MMLU-Pro	NeMo-Skills	1
AIME25	NeMo-Skills	10
GPQA:d	NeMo-Skills	5

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):

Table with columns: Metric, GPT-OSS-120B, Hypernova 60B 2605
Metric	GPT-OSS-120B	Hypernova 60B 2605
Concurrency	128	128
Throughput (tok/s)	3,821	5,210
TTFT (s)	7.04	4.85
Model weights (GB)	65	32

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

Performance

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

Table with columns: Field, Value
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}
}

Built by Multiverse Computing · Report an issue · Discord

Hypernova-60B-2605

README

Table of Contents

Model Overview

Technical Deep Dive

Key Characteristics

Quick Start

What’s New in HyperNova 60B 2605

Summary

Tool Calling

Example Tool Call

Architecture

Model Specifications

Evaluation

Benchmarks Results

HyperNova 60B Benchmark Comparison

Evaluation Methodology

Inference:

Inference Performance

Metrics reported

Performance evaluation conditions

Languages

Intended Use

Recommended Use Cases

Out-of-Scope Uses

Safety & Limitations

Known Limitations

Recommendations

Model Information

Citation

Explore FriendliAI today

README

Table of Contents

Model Overview

Technical Deep Dive

Key Characteristics

Quick Start

What’s New in HyperNova 60B 2605

Summary

Tool Calling

Example Tool Call

Architecture

Model Specifications

Evaluation

Benchmarks Results

HyperNova 60B Benchmark Comparison

Evaluation Methodology

Inference:

Inference Performance

Metrics reported

Performance evaluation conditions

Languages

Intended Use

Recommended Use Cases

Out-of-Scope Uses

Safety & Limitations

Known Limitations

Recommendations

Model Information

Citation