---
license: apache-2.0
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
library_name: transformers
base_model:
- arcee-ai/Trinity-Mini-Base
model-index:
- name: Trinity-Mini
results:
- task:
type: text-generation
dataset:
name: Benchmarks
type: benchmark
metrics:
- name: SimpleQA
type: simpleqa
value: 8.9
- name: MUSR
type: musr
value: 63.49
- name: MMLU (Zero Shot)
type: mmlu_zero_shot
value: 84.95
- name: Math-500
type: math_500
value: 92.1
- name: GPQA-Diamond
type: gpqa_diamond
value: 58.55
- name: BFCL V3
type: bfcl_v3
value: 59.67
source:
name: Model README
url: https://huggingface.co/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:** [Apache 2.0](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-train/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 Apache-2.0 license.
