---
license: apache-2.0
language:
- en
base_model:
- LLM360/K2-V2
---
# **K2-V2-Instruct**
๐ [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) - ๐ [Code](https://github.com/llm360/k2v2_train) - ๐ข [Project Page](https://huggingface.co/LLM360/K2-V2-Instruct)
K2-V2 is our most capable fully open model to date, and one of the strongest open-weight models in its class. It uses a 70B-parameter dense transformer architecture and represents the latest advancement in the LLM360 model family.
Beyond standard competencies such as factual knowledge and conversational ability, K2-V2 demonstrates strong long-context consistency, deep mathematical understanding, and robust reasoning skills. These capabilities serve as building blocks for sophisticated downstream applications, such as solving complex math problems and executing agentic workflows.
---
## **Quick Start**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("llm360/k2-v2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("llm360/k2-v2")
prompt = "Explain why the derivative of sin(x) is cos(x)."
messages = [
{"role": "system", "content": "You are K2, a helpful assistant created by Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) Institute of Foundation Models (IFM)."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
## **Evaluation Summary**
Below we report performance across general, reasoning, mathematical, and coding benchmarks. Scores for K2-V2 checkpoints (base โ mid-4) demonstrate the impact of staged mid-training on reasoning quality.
| Task / Model | base | mid-1 | mid-2 | mid-3 | mid-4 | Qwen2.5-72B | Llama3.0-70B | Llama3.1-70B | Olmo3-32B |
|--------------|------|-------|-------|-------|-------|--------------|---------------|---------------|------------|
| **General Tasks** | | | | | | | | | |
| **MMLU** | 74.3 | 74.4 | 73.5 | 75.0 | 75.2 | **86.1** | 79.5 | 79.3 | 75.2 |
| **MMLU-Pro** | 43.7 | 46.8 | 48.1 | **59.8** | 57.0 | 58.1 | 52.8 | 53.8 | 49.6 |
| **BBH** | 68.4 | 79.8 | 81.1 | 82.2 | 83.2 | **86.3** | 82.2 | 82.1 | 77.6 |
| **HELLASWAG** | 87.8 | 86.9 | 86.6 | 86.6 | 86.0 | 87.6 | **88.0** | 85.0 | 84.8 |
| **WINOGRANDE** | 82.6 | 83.7 | 83.7 | 83.7 | 83.0 | 83.9 | 85.3 | 79.8 | **90.3** |
| **PIQA** | 84.2 | 84.0 | 83.3 | 82.9 | 83.1 | 83.5 | 84.6 | 84.3 | **85.6** |
| **TRUTHFULQA** | 54.0 | 54.9 | 55.1 | 55.8 | 53.9 | **60.5** | 45.6 | 49.7 | 54.9 |
| **Math & STEM Tasks** | | | | | | | | | |
| **GPQA-DIAMOND** | 26.3 | 31.3 | 27.8 | 43.9 | **55.1** | 34.9 | 21.2 | 27.3 | 30.3 |
| **GSM8K** | 68.0 | 76.4 | 82.1 | **93.6** | 92.5 | 91.2 | 83.2 | 81.1 | 80.5 |
| **MATH** | 27.8 | 38.2 | 41.1 | **94.7** | 91.4 | 58.5 | 41.9 | 41.6 | 43.4 |
| **AIME 2025** | 0.0 | 17.6 | 25.1 | **53.2** | 46.9 | 1.7 | 0.1 | 0.2 | 14.7 |
| **ARC-CHALLENGE** | 64.9 | 66.4 | 66.4 | 66.0 | 66.3 | **72.4** | 69.2 | 64.9 | 65.4 |
| **Coding Tasks** | | | | | | | | | |
| **MBPP** | 57.6 | 57.8 | 58.2 | 59.8 | 61.8 | **75.4** | 69.2 | 64.4 | 60.2 |
| **HUMANEVAL** | 50.0 | 51.2 | 53.7 | **54.3** | **54.3** | **54.3** | 42.1 | 50.6 | 36.0 |
Below we report the evaluation results for K2-V2 after supervised fine-tuning (SFT). These variants correspond to three levels of reasoning effort (Low < Medium < High).
| Metric / Model | **K2 Low**
Dense ยท 70B | **K2 Medium**
Dense ยท 70B | **K2 High**
Dense ยท 70B | **Olmo3 Think SFT**
Dense ยท 32B ยท No RL | **Olmo3 Think**
Dense ยท 32B ยท RL | **Qwen3 235B**
MoE ยท 235B A22B ยท Reasoning | **Qwen3 235B 2507**
MoE ยท 235B A22B ยท Instruct |
|----------------|----------------|----------------|----------------|-----------------------------|------------------------------|------------------------------|-------------------------------|
| **LongBench V2** | 40.7 | 41.3 | 42.6 | 42.8 | 47.1 | 60.9 | 52.7 |
| **AIME25** | 27.3 | 62.0 | 80.2 | 68.3 | 73.3 | 88.8 | 67.9 |
| **HMMT25** | 19.0 | 45.6 | 71.4 | 43.3 | 50.83 | 84.2 | 50.63 |
| **GSM8K** | 92.4 | 92.0 | 94.8 | 96.1 | 95.7 | 93.5 | 94.9 |
| **Minerva** | 85.0 | 90.6 | 94.5 | 96.9 | 97.3 | 98.0 | 97.1 |
| **GPQA-D** | 48.5 | 60.6 | 69.3 | 58.0 | 59.8 | 80.7 | 73.9 |
| **MBPP** | 71.0 | 75.8 | 84.8 | 87.6 | 91.6 | 96.2 | 85.6 |
| **HumanEval** | 82.3 | 84.2 | 91.5 | 96.3 | 96.3 | 94.5 | 95.7 |
| **LCBv6** | 39.9 | 51.3 | 67.0 | 67.9 | 67.6 | 72.8 | 59.3 |
Please refer to our [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) for detailed evaluation results.
---
## **Datasets & Mixtures**
### **SFT Mix**
* **TxT360-3efforts**: curated instruction + mixed-difficulty reasoning traces
* Tool-calling demonstrations
* Small but high-value corpus to showcase model potential
All mixtures, filtering rules, and data sources are fully released for reproducibility.
Please refer to our [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) for detailed datasets and mixtures information.
---
## **Model Description**
- **Model type:** K2-V2 follows a standard decoder-only transformer with grouped-query attention and RMSNorm.
- **Training stage:** Pre-training & Post-training
- **Language(s) (NLP):** English
- **License:** Apache 2.0
| Model Hyperparameter | Value |
| ----------- | ----------- |
| Total Parameters | 70B |
| Hidden Size | 8,192 |
| Intermediate Size (FFN) | 28,672 |
| Number of Attention Heads | 64 |
| Number of Layers | 80 |
| RMSNorm ษ | 1e-5 |
| Pre-training Seq Length | 8,192 |
| Post-training Seq Length | 524,288 |
| Vocab Size | 250,000 |
---
## Citation
If you use K2-V2-Instruct in your research, please cite the following:
```
@misc{llm360_k2v2_2025,
title = {K2-V2: A 360-Open, Reasoning-Enhanced Open LLM},
author = {K2 Team},
year = {2025},
archivePrefix = {arXiv},
eprint = {XXXX.XXXXX},
primaryClass = {cs.CL}
}
```