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----
-license: apache-2.0
----
+---
+base_model:
+- openai/gpt-oss-120b
+- MultiverseComputingCAI/HyperNova-60B
+library_name: transformers
+license: apache-2.0
+---
+<div align="center">
+
+# HyperNova 60B 2605
+
+### 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-2605)
+[![Discord](https://img.shields.io/badge/Discord-Community-5865F2?logo=discord&logoColor=white)](https://discord.gg/cGas9uStqp)
+
+**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 2605](#whats-new-in-hypernova-60b-2605)
+- [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 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.](https://multiversecomputing.com/papers/hypernova-60b-2605-same-intelligence-half-the-size-improved-tool-calling-capability)
+
+---
+
+## Key Characteristics
+
+| 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
+
+| Specification     | Value              |
+|-------------------|--------------------|
+| Total parameters  | 60B, 4.8B active MoE |
+
+---
+
+## Evaluation & Benchmarks
+
+### Evaluation Methodology
+
+Benchmark scores were obtained with the following setups. Methodology varies by benchmark family.
+
+#### HLE, MMLU-Pro, AIME25, GPQA:d, LiveCodeBench
+
+- **Evaluation framework**: [Nemo-skills](https://github.com/NVIDIA/NeMo-Skills)
+- **Inference library**: vLLM 0.13.0  
+- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **Reasoning effort**: high  
+- **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 0.13.0  
+- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **Reasoning effort**: high  
+- **Decoding**: temperature = 1.0, max_tokens = 131072, top_p = 1.0, top_k = 0  
+- **Batch size**: 64
+  
+#### Tau2-bench (Telecom)
+
+- **Evaluation framework**: [EvalScope](https://github.com/EvalScope/EvalScope) 1.4.1
+- **Inference library**: vLLM 0.13.0  
+- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **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 == 0.13.0
+- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **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)
+
+
+| Benchmark             | gpt-oss-120b                  | HyperNova 60B 2602          | HyperNova 60B 2605       |
+|-----------------------|-------------------------------|-----------------------------|--------------------------|
+| HLE                   | 18.50                         | 7.28                        | 14.97                    |
+| MMLU-Pro              | 79.64                         | 74.25                       | 76.77                    |
+| Tau2-bench (Telecom)  | 63.74                         | 60.53                       | 61.70                    |
+| AIME25                | 93.67                         | 86.00                       | 90.00                    |
+| GPQA:d                | 74.64                         | 65.56                       | 71.92                    |
+| IFBench               | 67.01                         | 59.40                       | 66.57                    |
+| SciCode               | 41.52                         | 33.53                       | 36.00                    |
+| LiveCodeBench         | 62.75                         | 51.53                       | 68.68                    |
+| Terminal Bench        | 24.24                         | 12.12                       | 15.91                    |
+| AA-LCR                | 49.00                         | 35.67                       | 40.33                    |
+
+![Intelligence](assets/intelligence.png)
+
+### Quantitative Results (Inference Performance)
+
+#### Metrics reported
+
+- **System Output Throughput (higher is better)**: Mean output tokens per second across all concurrent requests over the benchmarking phase.
+- **End-to-End Latency per Query (lower is better):** Median end-to-end response time for each query from the time the query is sent.
+- **Output Speed per Query (higher is better):** Median output tokens per second after the first token is received for each query.
+- **Time to first token (TTFT) (lower is better):** Median time to first token.
+- **Estimated total memory — (lower is better):** Median from each GuideLLM phase (estimated total footprint: weights plus KV contribution from monitored usage).
+- **Model weights (lower is better):** 
+
+On the same hardware and harness, **HyperNova 60B 2605** is compared to **gpt-oss-120b** using GuideLLM. Each table lists **median** values for that model at each **concurrency phase** (1 → 256 concurrent requests).
+
+**gpt-oss-120b**
+
+| Concurrency | Throughput (tok/s) | E2E latency (s) | Output speed (tok/s) | TTFT (s) | Est. total memory (GB) | Model weights (GB) |
+|------------:|-------------------:|----------------:|-----------------------:|---------:|------------------------:|-------------------:|
+| 1 | 173 | 3.02 | 387.1 | 1.51 | 62.0 | 61.6 |
+| 2 | 292 | 3.89 | 372.1 | 1.78 | 62.4 | 61.6 |
+| 4 | 453 | 5.26 | 208.0 | 2.23 | 63.2 | 61.6 |
+| 8 | 643 | 6.47 | 181.7 | 3.02 | 64.8 | 61.6 |
+| 16 | 897 | 11.21 | 102.5 | 4.28 | 68.1 | 61.6 |
+| 32 | 1114 | 15.51 | 75.1 | 6.25 | 74.6 | 61.6 |
+| 64 | 1404 | 24.32 | 52.1 | 10.17 | 87.6 | 61.6 |
+| 128 | 1828 | 42.99 | 28.3 | 18.23 | 114.0 | 61.6 |
+| 192 | 1818 | 61.47 | 29.8 | 38.43 | 113.9 | 61.6 |
+| 256 | 1842 | 81.04 | 29.5 | 57.45 | 114.0 | 61.6 |
+
+**HyperNova 60B 2605**
+
+| Concurrency | Throughput (tok/s) | E2E latency (s) | Output speed (tok/s) | TTFT (s) | Est. total memory (GB) | Model weights (GB) |
+|------------:|-------------------:|----------------:|-----------------------:|---------:|------------------------:|-------------------:|
+| 1 | 179 | 2.12 | 336.3 | 1.20 | 32.1 | 31.8 |
+| 2 | 304 | 2.21 | 457.9 | 1.44 | 32.4 | 31.8 |
+| 4 | 487 | 2.91 | 305.8 | 1.76 | 33.0 | 31.8 |
+| 8 | 740 | 3.84 | 207.8 | 2.31 | 34.1 | 31.8 |
+| 16 | 982 | 5.74 | 142.0 | 3.37 | 36.5 | 31.8 |
+| 32 | 1233 | 8.46 | 101.7 | 5.25 | 41.1 | 31.8 |
+| 64 | 1482 | 14.14 | 54.2 | 8.60 | 50.4 | 31.8 |
+| 128 | 1923 | 25.03 | 32.0 | 15.09 | 69.0 | 31.8 |
+| 192 | 1808 | 37.88 | 24.5 | 23.93 | 87.6 | 31.8 |
+| 256 | 1716 | 52.16 | 18.8 | 31.89 | 106.5 | 31.8 |
+
+
+
+#### Performance evaluation conditions
+
+Our performance evaluation follows the spirit of [Artificial Analysis](https://artificialanalysis.ai/methodology/system-load-test).
+
+- **Inference library**: vLLM 0.13.0
+- **Monitoring libraries**: GuideLLM, nvidia-ml-py
+- **Hardware**: 1× NVIDIA H200 Tensor Core GPU
+- **Conditions**: **concurrency phases** 1, 2, 4, 8, 16, 32, 64, 128, 192, and 256 concurrent requests (one GuideLLM phase each)
+- **Phase duration**: Each phase lasts 3 minutes (excluding ramp-up and cool-down periods).
+- **Workload shape:** input length is ~1000 tokens per query (median); median output length varies by phase and model.
+- **Streaming**: Benchmarking is conducted with streaming enabled.
+
+The figure below is a **side-by-side comparison at concurrency = 128 only** 
+
+![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
+
+- **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](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.  
+
+### 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 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](https://www.multiversecomputing.com)** · [Report an issue](https://huggingface.co/MultiverseComputingCAI/HyperNova-60B-2605/discussions) · [Discord](https://discord.gg/8mT9FveN)