---
title: Trinity-Mini
canonical_url: "https://www.modelscope.cn/models/arcee-ai/Trinity-Mini"
md_url: "https://www.modelscope.cn/models/arcee-ai/Trinity-Mini.md"
repository: arcee-ai/Trinity-Mini
last_updated: 2026-05-29
license: other
pipeline_tag: text-generation
tasks:
- text-generation
model_type:
- afmoe
architectures:
- AfmoeForCausalLM
base_model:
- arcee-ai/Trinity-Mini-Base
base_model_relation: finetune
parameters: 26.1B
tensor_type:
- BF16
library_name:
- pytorch
- transformer
- safetensors
frameworks:
- pytorch
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
downloads: 187
stars: 0
---
# Trinity-Mini
> Trinity-Mini - arcee-ai 在 ModelScope 开源的模型。Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
arcee-ai/Trinity-Mini 是 ModelScope 魔搭社区上的 26.1B 参数text-generation模型，采用 other 许可，基于 arcee-ai/Trinity-Mini-Base 构建。
- **Repository**: arcee-ai/Trinity-Mini
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 26.1B
- **Base model**: arcee-ai/Trinity-Mini-Base
- **Downloads**: 187
- **Stars**: 0
- **Last updated**: 2026-05-29
Source: https://www.modelscope.cn/models/arcee-ai/Trinity-Mini
---
# Trinity Mini
Trinity Mini is an Arcee AI 26B MoE model with 3B active parameters. It is the medium-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
This model is tuned for reasoning, but in testing, it uses a similar total token count to competitive instruction-tuned models.
***
Trinity Mini is trained on 10T tokens gathered and curated through a key partnership with [Datology](https://www.datologyai.com/), building upon the excellent dataset we used on [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) with additional math and code.
Training was performed on a cluster of 512 H200 GPUs powered by [Prime Intellect](https://www.primeintellect.ai/) using HSDP parallelism.
More details, including key architecture decisions, can be found on our blog [here](https://www.arcee.ai/blog/the-trinity-manifesto)
Try it out now at [chat.arcee.ai](http://chat.arcee.ai/)
***
## Model Details
* **Model Architecture:** AfmoeForCausalLM
* **Parameters:** 26B, 3B active
* **Experts:** 128 total, 8 active, 1 shared
* **Context length:** 128k
* **Training Tokens:** 10T
* **License:** [OpenMDW-1.1](https://huggingface.co/arcee-ai/Trinity-Mini#license)
* **Recommended settings:**
* temperature: 0.15
* top_k: 50
* top_p: 0.75
* min_p: 0.06
***
## Benchmarks
### Running our model
- [Transformers](https://huggingface.co/arcee-ai/Trinity-Mini#transformers)
- [VLLM](https://huggingface.co/arcee-ai/Trinity-Mini#vllm)
- [llama.cpp](https://huggingface.co/arcee-ai/Trinity-Mini#llamacpp)
- [LM Studio](https://huggingface.co/arcee-ai/Trinity-Mini#lm-studio)
- [API](https://huggingface.co/arcee-ai/Trinity-Mini#api)
## Transformers
Use the `main` transformers branch
```
git clone https://github.com/huggingface/transformers.git
cd transformers
# pip
pip install '.[torch]'
# uv
uv pip install '.[torch]'
```
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "arcee-ai/Trinity-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
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=0.5,
top_k=50,
top_p=0.95
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
If using a released transformers, simply pass "trust_remote_code=True":
```python
model_id = "arcee-ai/Trinity-Mini"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
```
## VLLM
Supported in VLLM release 0.11.1
```
# pip
pip install "vllm>=0.11.1"
```
Serving the model with suggested settings:
```
vllm serve arcee-ai/Trinity-Mini \
--dtype bfloat16 \
--enable-auto-tool-choice \
--reasoning-parser deepseek_r1 \
--tool-call-parser hermes
```
## llama.cpp
Supported in llama.cpp release b7061
Download the latest [llama.cpp release](https://github.com/ggml-org/llama.cpp/releases)
```
llama-server -hf arcee-ai/Trinity-Mini-GGUF:q4_k_m \
--temp 0.15 \
--top-k 50 \
--top-p 0.75
--min-p 0.06
```
## LM Studio
Supported in latest LM Studio runtime
Update to latest available, then verify your runtime by:
1. Click "Power User" at the bottom left
2. Click the green "Developer" icon at the top left
3. Select "LM Runtimes" at the top
4. Refresh the list of runtimes and verify that the latest is installed
Then, go to Model Search and search for `arcee-ai/Trinity-Mini-GGUF`, download your prefered size, and load it up in the chat
## API
Trinity Mini is available today on openrouter:
https://openrouter.ai/arcee-ai/trinity-mini
```
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-mini",
"messages": [
{
"role": "user",
"content": "What are some fun things to do in New York?"
}
]
}'
```
## License
Trinity-Mini is released under the OpenMDW-1.1 license.
