README.md

---
license: apache-2.0
language:
- en
---

# **K2-V2**

<img src="figures/K2.LOGO.PRIMARY.RGB.png" width="100" alt="K2-V2 model logo"/>

📚 [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) - 📝 [Code](https://github.com/llm360/k2v2_train) - 🏢 [Project Page](https://huggingface.co/LLM360/K2-V2) 

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.

<img src="figures/sft-models.png" width="400" alt="K2-V2 SFT results"/>

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.

<img src="figures/base-models.png" width="400" alt="K2-V2 GPQA results"/>

---

## **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)."
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** | <u>79.5</u> | 79.3 | 75.2 |
| **MMLU-Pro** | 43.7 | 46.8 | 48.1 | **59.8** | 57.0 | <u>58.1</u> | 52.8 | 53.8 | 49.6 |
| **BBH** | 68.4 | 79.8 | 81.1 | 82.2 | <u>83.2</u> | **86.3** | 82.2 | 82.1 | 77.6 |
| **HELLASWAG** | <u>87.8</u> | 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 | <u>85.3</u> | 79.8 | **90.3** |
| **PIQA** | 84.2 | 84.0 | 83.3 | 82.9 | 83.1 | 83.5 | <u>84.6</u> | 84.3 | **85.6** |
| **TRUTHFULQA** | 54.0 | 54.9 | 55.1 | <u>55.8</u> | 53.9 | **60.5** | 45.6 | 49.7 | 54.9 |
| **Math & STEM Tasks** | | | | | | | | | |
| **GPQA-DIAMOND** | 26.3 | 31.3 | 27.8 | <u>43.9</u> | **55.1** | 34.9 | 21.2 | 27.3 | 30.3 |
| **GSM8K** | 68.0 | 76.4 | 82.1 | **93.6** | <u>92.5</u> | 91.2 | 83.2 | 81.1 | 80.5 |
| **MATH** | 27.8 | 38.2 | 41.1 | **94.7** | <u>91.4</u> | 58.5 | 41.9 | 41.6 | 43.4 |
| **AIME 2025** | 0.0 | 17.6 | 25.1 | **53.2** | <u>46.9</u> | 1.7 | 0.1 | 0.2 | 14.7 |
| **ARC-CHALLENGE** | 64.9 | 66.4 | 66.4 | 66.0 | 66.3 | **72.4** | <u>69.2</u> | 64.9 | 65.4 |
| **Coding Tasks** | | | | | | | | | |
| **MBPP** | 57.6 | 57.8 | 58.2 | 59.8 | 61.8 | **75.4** | <u>69.2</u> | 64.4 | 60.2 |
| **HUMANEVAL** | 50.0 | 51.2 | <u>53.7</u> | **54.3** | **54.3** | **54.3** | 42.1 | 50.6 | 36.0 |


Please refer to our [Tech Report](https://www.llm360.ai/reports/K2_V2_report.pdf) 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**

* Check out https://huggingface.co/LLM360/K2-V2-Instruct

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
- **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}
}
```