---
title: Trinity-Large-Preview-GGUF
canonical_url: "https://www.modelscope.cn/models/unsloth/Trinity-Large-Preview-GGUF"
md_url: "https://www.modelscope.cn/models/unsloth/Trinity-Large-Preview-GGUF.md"
repository: unsloth/Trinity-Large-Preview-GGUF
last_updated: 2026-01-29
license: apache-2.0
base_model_relation: quantized
library_name:
  - pytorch
  - transformer
  - gguf
frameworks:
  - pytorch
language:
  - en
  - es
  - fr
  - de
  - it
  - pt
  - ru
  - ar
  - hi
  - ko
  - zh
downloads: 243
stars: 0
---

# Trinity-Large-Preview-GGUF

> Trinity-Large-Preview-GGUF - unsloth 在 ModelScope 开源的模型。[!NOTE] Includes Unsloth chat template fixes! For llama.cpp, use --jinja

unsloth/Trinity-Large-Preview-GGUF 是 ModelScope 魔搭社区上的机器学习模型，采用 apache-2.0 许可。

- **Repository**: unsloth/Trinity-Large-Preview-GGUF
- **License**: apache-2.0
- **Downloads**: 243
- **Stars**: 0
- **Last updated**: 2026-01-29

Source: https://www.modelscope.cn/models/unsloth/Trinity-Large-Preview-GGUF

---

> [!NOTE]
>  Includes Unsloth **chat template fixes**! <br> For `llama.cpp`, use `--jinja`
>

<div>
<p style="margin-top: 0;margin-bottom: 0;">
    <em><a href="https://docs.unsloth.ai/basics/unsloth-dynamic-v2.0-gguf">Unsloth Dynamic 2.0</a> achieves superior accuracy & outperforms other leading quants.</em>
  </p>
  <div style="display: flex; gap: 5px; align-items: center; ">
    <a href="https://github.com/unslothai/unsloth/">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png" width="133">
    </a>
    <a href="https://discord.gg/unsloth">
      <img src="https://github.com/unslothai/unsloth/raw/main/images/Discord%20button.png" width="173">
    </a>
    <a href="https://docs.unsloth.ai/">
      <img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="143">
    </a>
  </div>
</div>

<!-- markdownlint-disable first-line-h1 -->
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<div align="center">
  <picture>
    <img
      src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
      alt="Arcee Trinity Large"
      style="max-width: 100%; height: auto;"
    >
  </picture>
</div>
<hr>

# Trinity-Large-Preview

## Introduction

Trinity-Large-Preview is a 398B-parameter sparse Mixture-of-Experts (MoE) model with approximately 13B active parameters per token. It is the largest model in Arcee AI's Trinity family, trained on more than 17 trillion tokens and delivering frontier-level performance with strong long-context comprehension.
Trinity-Large-Preview is a lightly post-trained model based on Trinity-Large-Base.

Try it at [chat.arcee.ai](http://chat.arcee.ai/)

More details on the training of Trinity Large are available in the [technical report](https://github.com/arcee-ai/trinity-large-tech-report/).


## Model Variants

The Trinity Large family consists of three checkpoints from the same training run:

- **Trinity-Large-Preview** (this release): Lightly post-trained, chat-ready model undergoing active RL
- **[Trinity-Large-TrueBase](https://huggingface.co/arcee-ai/Trinity-Large-TrueBase)**: 10T-token pre-anneal pretraining checkpoint
- **[Trinity-Large-Base](https://huggingface.co/arcee-ai/Trinity-Large-Base)**: Full 17T-token pretrained foundation model with mid-training anneals

## Architecture

Trinity-Large-Preview uses a sparse MoE configuration designed to maximize efficiency while maintaining large-scale capacity.

| Hyperparameter | Value |
|:---|:---:|
| Total parameters | ~398B |
| Active parameters per token | ~13B |
| Experts | 256 (1 shared) |
| Active experts | 4 |
| Routing strategy | 4-of-256 (1.56% sparsity) |
| Dense layers | 6 |
| Pretraining context length | 8,192 |
| Context length after extension | 512k |
| Architecture | Sparse MoE (AfmoeForCausalLM) |

## Benchmarks

| Benchmark | Llama 4 Maverick | Trinity-Large Preview |
|-----------|------------------|----------------------|
| MMLU | 85.5 | 87.2 |
| MMLU-Pro | 80.5 | 75.2 |
| GPQA-Diamond | 69.8 | 63.3 |
| AIME 2025 | 19.3 | 24.0 |

## Training Configuration

### Pretraining

- Training tokens: 17 trillion
- Data partner: [Datology](https://www.datologyai.com/)

<div align="center">
  <picture>
      <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/sSVjGNHfrJKmQ6w8I18ek.png" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Datology">
  </picture>
</div>

## Posttraining
- This checkpoint was instruction tuned on 20B tokens.

### Infrastructure

- Hardware: 2,048 NVIDIA B300 GPUs
- Parallelism: HSDP + Expert Parallelism
- Compute partner: [Prime Intellect](https://www.primeintellect.ai/)


<div align="center">
  <picture>
      <img src="https://cdn-avatars.huggingface.co/v1/production/uploads/61e020e4a343274bb132e138/H2mcdPRWtl4iKLd-OYYBc.jpeg" style="background-color:ghostwhite;padding:5px;" width="17%" alt="Powered by Prime Intellect">
  </picture>
</div>

## Usage

### Running our model

- [Transformers](https://huggingface.co/arcee-ai/Trinity-Large-Preview#transformers)
- [VLLM](https://huggingface.co/arcee-ai/Trinity-Large-Preview#vllm)
- [llama.cpp](https://huggingface.co/arcee-ai/Trinity-Large-Preview#llamacpp)
- [LM Studio](https://huggingface.co/arcee-ai/Trinity-Large-Preview#lm-studio)
- [API](https://huggingface.co/arcee-ai/Trinity-Large-Preview#api)


### Transformers

Use the `main` transformers branch or pass `trust_remote_code=True` with a released version.

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "arcee-ai/Trinity-Large-Preview"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role": "user", "content": "Who are you?"},
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.8,
    top_k=50,
    top_p=0.8
)

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### VLLM

Supported in VLLM release 0.11.1+

```bash
vllm serve arcee-ai/Trinity-Large-Preview \
  --dtype bfloat16 \
  --enable-auto-tool-choice \
  --tool-call-parser hermes
```

### llama.cpp

Supported in llama.cpp release b7061+

```bash
llama-server -hf arcee-ai/Trinity-Large-Preview-GGUF:q4_k_m
```

### LM Studio

Supported in the latest LM Studio runtime. Search for `arcee-ai/Trinity-Large-Preview-GGUF` in Model Search.

### API

Available on OpenRouter:

```bash
curl -X POST "https://openrouter.ai/v1/chat/completions" \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "arcee-ai/trinity-large-preview",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
  }'
```


## License

Trinity-Large-Preview is released under the Apache License, Version 2.0.

## Citation

```bibtex
@misc{arcee_trinity_large_preview,
  title = {Trinity-Large-Preview},
  author = {{Arcee AI}},
  year = {2026},
  note = {398B sparse MoE model trained on 17T tokens}
}
```