Instructions to use arcee-ai/Trinity-Nano-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arcee-ai/Trinity-Nano-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="arcee-ai/Trinity-Nano-Base", 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-Nano-Base", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("arcee-ai/Trinity-Nano-Base", trust_remote_code=True, device_map="auto") 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 Settings
- vLLM
How to use arcee-ai/Trinity-Nano-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "arcee-ai/Trinity-Nano-Base" # 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-Nano-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/arcee-ai/Trinity-Nano-Base
- SGLang
How to use arcee-ai/Trinity-Nano-Base 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-Nano-Base" \ --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-Nano-Base", "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-Nano-Base" \ --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-Nano-Base", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use arcee-ai/Trinity-Nano-Base with Docker Model Runner:
docker model run hf.co/arcee-ai/Trinity-Nano-Base
File size: 2,950 Bytes
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license: other
language:
- en
- es
- fr
- de
- it
- pt
- ru
- ar
- hi
- ko
- zh
library_name: transformers
base_model:
- arcee-ai/Trinity-Nano-Base-Pre-Anneal
license_link: LICENSE
license_name: openmdw-1.1
---
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<picture>
<img
src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png"
alt="Arcee Trinity Mini"
style="max-width: 100%; height: auto;"
>
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# Trinity Nano Base
Trinity Nano is an Arcee AI 6B MoE model with 1B active parameters. It is the small-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.
This base model *pre* fine tuning, and so is not suitable for chatting, and should be trained for your specific domain before use.
Trinity Nano 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)
***
## Model Details
* **Model Architecture:** AfmoeForCausalLM
* **Parameters:** 6B, 1B active
* **Experts:** 128 total, 8 active, 1 shared
* **Context length:** 128k
* **Training Tokens:** 10T
* **License:** [OpenMDW-1.1](https://huggingface.co/arcee-ai/Trinity-Nano-Base#license)
## Benchmarks
### 🔢 Math & Reasoning
| Benchmark | Score |
|-----------|-------|
| GSM8K | 58.4% |
| Minerva Math 500 | 36.0% |
| DROP (0-shot) | 4.5% |
| DROP (5-shot) | 63.6% |
### 💻 Code Generation
| Benchmark | Pass@1 | Pass@10 |
|-----------|--------|---------|
| HumanEval (3-shot, bpb) | 36.3% (bpb) | - |
| HumanEval+ (temp 0.8) | 31.7% | - |
| MBPP+ | 44.7% | - |
### 🧠 Knowledge & Reasoning
| Benchmark | 5-shot | 0-shot |
|-----------|---------|--------|
| ARC-Challenge | 84.0% | 78.2% |
| ARC-Easy | 94.8% | 91.2% |
| CommonsenseQA | 74.9% | 62.7% |
| OpenBookQA | 82.2% | 75.2% |
| WinoGrande | 72.8% | 68.0% |
| MMLU | 67.7% | 64.2% |
| MMLU Pro | 35.8% | 27.7% |
| AGI Eval (English) | 51.8% | - |
| BBH (CoT) | 50.4% | 7.6% |
### 📘 Understanding & QA
| Benchmark | Score |
|-----------|-------|
| BoolQ (5-shot) | 84.3% |
| HellaSwag (5-shot) | 77.4% |
| PIQA (5-shot) | 82.2% |
| SciQ (5-shot) | 93.2% |
| Social IQA (5-shot) | 73.0% |
<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">
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</div>
## License
Trinity-Mini-Base is released under the OpenMDW-1.1 license. |