README.md

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<p align="center">
<picture>
  <img src="https://raw.githubusercontent.com/trillion-labs/.github/main/Tri-21B.png" alt="Tri-21B", style="width: 80%;">
</picture>
</p>

## Introduction

**Tri-21B**를 4bit으로 양자화한 모델

We introduce **Tri-21B**, our flagship large language model that redefines the efficiency frontier in LLM training. By achieving state-of-the-art performance with only 2.3T training tokens, we demonstrate that exceptional capabilities don't require excessive computational resources.

<p align="center">
<img src="https://raw.githubusercontent.com/trillion-labs/.github/main/pareto-2507.png" alt="Average Performance vs. Approximate Training FLOPs" style="width: 100%; max-width: 1400px;">
</p>


### Key Highlights
* **Unprecedented Training Efficiency**: Trained on just 2.3T tokens—significantly less than comparable models—while achieving 70.3% average accuracy across MMLU/KMMLU/Global MMLU benchmarks
* **Pushing the Pareto Frontier**: With only 2.95E+23 FLOPs, Tri-21B outperforms models requiring 2-10x more compute, setting a new standard for efficient scaling
* **Enhanced Reasoning**: Modified training dataset mixture specifically optimized for reasoning capabilities
* **Advanced Post-Training**: Significantly improved RL training pipeline focusing on mathematical reasoning and everyday usage
* **Multi-lingual**: Specially optimized for Korean, English, and Japanese.

Our **Tri-21B** represents a paradigm shift in efficient model development. When comparing performance to training FLOPs, our model dramatically pushes the Pareto frontier—achieving performance comparable to or exceeding models like Qwen2.5-32B (74.6% at 3.46E+24 FLOPs) and Gemma 3 IT 27B (67.6% at 2.27E+24 FLOPs) while using approximately 8-12x fewer computational resources.


### Model Specifications

#### Tri-21B
- Type: Causal Language Model
- Training Stage: Pre-training & Post-training
- Architecture: Transformer Decoder with RoPE, SwiGLU, RMSNorm, and GQA
- Number of Parameters: 20.73B
- Number of Layers: 32
- Number of Attention Heads: 32 (Query) / 8 (Key, Value)
- Context Length: 8,192
- Number of Tokens Seen: 2.3T
- Vocab Size: 124,416 

## Training Efficiency Analysis

Our approach to training efficiency sets new benchmarks in the field. The following comparison demonstrates how Tri-21B achieves superior performance per FLOP compared to other state-of-the-art models of similar scale:

| Model | FLOPs | Avg. Accuracy¹ | Efficiency Ratio² |
|:------|:------|:--------------|:-----------------|
| **Tri-21B** | **2.95E+23** | **70.3%** | **1.00x (baseline)** |
| Gemma2-9b | 4.42E+23 | 61.5% | 0.48x |
| Qwen2.5-7B | 8.22E+23 | 63.4% | 0.29x |
| Exaone-3.5-32B | 1.25E+24 | 58.5% | 0.19x |
| Gemma 3 IT 27B | 2.27E+24 | 67.6% | 0.11x |
| Qwen2.5-32B | 3.46E+24 | 74.6% | 0.10x |
| Qwen3-32B | 5.77E+24 | 73.5% | 0.06x |

¹ Average of MMLU / KMMLU / Global MMLU (ja)  
² Performance per FLOP relative to Tri-21B

This efficiency breakthrough enables organizations to deploy state-of-the-art language models without the traditional computational barriers, democratizing access to advanced AI capabilities.


## Quickstart

Here is a code snippet with `apply_chat_template` that demonstrates how to load the tokenizer and model and generate text.

### Tri-21B Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "trillionlabs/Tri-21B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Explain the concept of quantum computing in simple terms."
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

### vLLM, SGLang Deployment

Tri-21B is also available with [vLLM](https://docs.vllm.ai/en/latest/) and [SGLang](https://docs.sglang.ai/)!

```bash
# vLLM
vllm serve trillionlabs/Tri-21B --dtype bfloat16 --max-model-len 8192

# vLLM with custom options
vllm serve trillionlabs/Tri-21B \
    --dtype bfloat16 \
    --max-model-len 8192 \
    --gpu-memory-utilization 0.95 \
    --port 8000
````

```bash
# SGLang
python3 -m sglang.launch_server --model-path trillionlabs/Tri-21B --dtype bfloat16

# SGLang with custom options
python3 -m sglang.launch_server \
    --model-path trillionlabs/Tri-21B \
    --dtype bfloat16 \
    --context-length 8192 \
    --port 30000 \
    --host 0.0.0.0
```

## Evaluation


We evaluated Tri-21B across a comprehensive suite of benchmarks assessing general reasoning, knowledge recall, coding abilities, mathematical reasoning, and instruction-following capabilities. We compare our model against state-of-the-art models of similar scale: Gemmma-3-IT-27B and Qwen3-32B to demonstrate its competitive performance.

<details>
<summary> Full evaluation settings </summary>
# Benchmark Evaluation Settings

| Benchmark | Language | Evaluation Setting | Metric |
|:----------|:---------|:------------------|:-------|
| **General Reasoning and Factuality** | | | |
| • HellaSwag | English | 0-shot | accuracy |
| • ARC:C | English | 0-shot | accuracy |
| • HAERAE | Korean | 3-shot | accuracy |
| • CLIcK | Korean | 0-shot | accuracy |
| • KoBEST | Korean | 5-shot | accuracy |
| **Knowledge and Reasoning** | | | |
| • KMMLU | Korean | 5-shot (0-shot, CoT) | accuracy (exact-match) |
| • MMLU | English | 5-shot (0-shot, CoT) | accuracy (exact-match) |
| • MMLU-Pro | English | 0-shot, CoT | exact-match |
| • Global-MMLU-Lite-ja | Japaneses | 5-shot | accuracy |
| **Coding** | | | |
| • HumanEval | English | 0-shot | pass@1 |
| • MBPPPlus | English | 0-shot | pass@1 |
| **Mathematical Reasoning** | | | |
| • GSM8k | English | 0-shot, CoT | exact-match |
| • MATH | English | 0-shot, CoT | exact-match |
| • GPQA | English | 4-shot | accuracy |
| • GPQA Diamond | English | 0-shot, CoT | accuracy |
| • HRM8k | Korean | 0-shot, CoT | exact-match |
| **Instruction Following and Chat** | | | |
| • IFEval | English | 0-shot | strict-average |
| • koIFEval | Korean | 0-shot | strict-average |
| • MT-Bench | English | LLM-as-a-judge (gpt-4o) | LLM score |
| • KO-MT-Bench | Korean | LLM-as-a-judge (gpt-4o) | LLM score |
| • systemIFEval | English | 0-shot | strict-average |

- *Note that koIFEval, systemIFEval, and KoRuler are our in-house evaluation benchmarks adapted for Korean to better assess model capabilities in Korean language tasks.
- **Note that MT-Bench, KO-MT-Bench, and LogicKor use a 10-point scale.

</details>

### Benchmark Results

Models compared:

- **Tri-21B**: Our flagship 21B parameter model
- **Qwen3-32B**: Qwen's 32B parameter model
- **Gemma3-IT-27B**: Google's Gemma 3 instruction-tuned 27B model


### General Reasoning and Factuality

| Benchmark | Tri-21B | Qwen3-32B | Gemma3-IT-27B |
| --- | --- | --- | --- |
| HAERAE | 86.16 | 71.67 | 78.09 | 
| KoBEST | 85.92 | 83.39 | 87.66 | 
| CLIcK | 72.32 | 66.89 | 67.54 | 
| KMMLU | 61.89 (69.90) | 61.73 (67.55)| 55.03 (60.61)| 
| MMLU | 77.62 (85.02) | 81.86 (84.46) | 77.42 (84.09) | 
| MMLU-Pro | 64.74 | 70.53 | 64.26 | 
| Global-MMLU-Lite-ja | 70.25 | 77.00 | 72.00 | 

### Coding

| Benchmark | Tri-21B | Qwen3-32B | Gemma3-IT-27B |
| --- | --- | --- | --- |
| HumanEval | 75.61 | 74.39 | 87.80 | 
| MBPPPlus | 73.02 | 74.40 | 84.92 | 

### Mathematical Reasoning

| Benchmark | Tri-21B | Qwen3-32B | Gemma3-IT-27B |
| --- | --- | --- | --- |
| GSM8k | 87.95 | 86.66 | 90.52 | 
| MATH | 77.60 | 81.40 | 85.00 | 
| GPQA | 39.73 | 41.07 | 37.95 | 
| GPQA-Diamond | 44.95 | 54.04 | 44.44 | 
| HRM8k | 56.70 | 66.24 | 63.90 | 

### Instruction Following and Chat

| Benchmark | Tri-21B | Qwen3-32B | Gemma3-IT-27B |
| --- | --- | --- | --- |
| IFEval | 80.75 | 86.08 | 80.78 | 
| koIFEval | 66.51 | 62.93 | 69.24 | 
| MT-Bench | 8.21 | 8.52 | 8.53 | 
| KO-MT-Bench | 7.79 | 8.47 | 8.46 | 
| systemIFEval | 77.40 | 77.92 | 77.94 | 

### Base Model Evaluation

The following table shows the performance of Tri-21B base model (before instruction tuning) on key benchmarks:

| Benchmark | Tri-21B Base |
| --- | --- |
| MMLU | 76.99 |
| KMMLU | 62.37 |
| KoBEST | 85.07 |
| BBH | 77.19 |
| GSM8K | 70.36 |
| MBPPPlus | 75.40 | 

## Limitations

- Language Support: The models are optimized for English, Korean, and Japanese. Usage with other languages may result in degraded performance.
- Knowledge Cutoff: The model's information is limited to data available up to Febuary, 2025.

## License
This model repository is licensed under the Trillion License.

## Contact
For inquiries, please contact: info@trillionlabs.co