Instructions to use arcee-ai/Trinity-Mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Trinity-Mini with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="arcee-ai/Trinity-Mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Trinity-Mini", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Trinity-Mini", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use arcee-ai/Trinity-Mini with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Trinity-Mini"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/arcee-ai/Trinity-Mini

SGLang

How to use arcee-ai/Trinity-Mini with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "arcee-ai/Trinity-Mini" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "arcee-ai/Trinity-Mini" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Trinity-Mini",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use arcee-ai/Trinity-Mini with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Trinity-Mini
```

davidlms commited on Dec 7, 2025

Commit

c143b9c

verified ·

1 Parent(s): 3aabb07

Add model-index with benchmark evaluations

Browse files

Added structured evaluation results from benchmark image:
- SimpleQA: 8.90
- MUSR: 63.49
- MMLU (Zero Shot): 84.95
- Math-500: 92.10
- GPQA-Diamond: 58.55
- BFCL V3: 59.67

This enables the model to appear in leaderboards and makes it easier to compare with other models.

Files changed (1) hide show

README.md +30 -0

README.md CHANGED Viewed

@@ -15,6 +15,36 @@ language:
 library_name: transformers
 base_model:
 - arcee-ai/Trinity-Mini-Base
 ---
 <div align="center">
   <picture>

 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
 ---
 <div align="center">
   <picture>