license: apache-2.0
language:
- en
K2-V2
📚 Tech Report - 📝 Code - 🏢 Project Page
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
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)."
inputs = tokenizer(prompt, 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 → 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 |
Please refer to our Tech Report for detailed evaluation results.
Datasets & Mixtures
K2-V2 training is organized into three stages, each using a transparent, publicly released mixture:
Pretraining Mix
- Large-scale natural text corpus spanning web content, books, code, and multilingual sources
- Mixture designed for stable scaling and broad general-knowledge coverage
- ~12T tokens
Mid-Training Mix
- TxT360-Midas: reasoning-oriented + long-context extensions
- Domain-focused sources: math, programming, scientific literature
- Synthetic expansions where natural data is scarce
SFT Mix
All mixtures, filtering rules, and data sources are fully released for reproducibility.
Please refer to our Tech Report 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
- 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 ɛ | 1e-5 |
| Pre-training Seq Length | 8,192 |
| Max Mid-training Seq Length | 524,288 |
| Vocab Size | 250,000 |
Intended Use
K2-V2 is designed for:
- research on large language models and reasoning
- downstream fine-tuning (e.g., instruction following, agents, domain models)
- experimentation with long-context architectures
- open, transparent benchmarking of LLM scaling
K2-V2 is not instruction-tuned. For aligned conversational use, please see K2-V2-Instruct.
Limitations
- May generate incorrect or hallucinated content, especially when asked about facts not seen during training
- Not optimized for safety, moderation, or refusal behavior (base model)
- Long-context performance depends on prompt quality and retrieval structure
- Primarily trained on English; multilingual capabilities are limited
- Inference cost is high due to the 70B parameter size
Citation
If you use K2-V2 in your research, please cite the following:
@misc{llm360_k2v2_2025,
title = {K2-V2: A 360-Open, Reasoning-Enhanced Open Foundation Model},
author = {K2 Team},
year = {2025},
archivePrefix = {arXiv},
eprint = {XXXX.XXXXX},
primaryClass = {cs.CL}
}