Instructions to use adinetwork/adi-v0.1-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adinetwork/adi-v0.1-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="adinetwork/adi-v0.1-base")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("adinetwork/adi-v0.1-base", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use adinetwork/adi-v0.1-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "adinetwork/adi-v0.1-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adinetwork/adi-v0.1-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/adinetwork/adi-v0.1-base
- SGLang
How to use adinetwork/adi-v0.1-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 "adinetwork/adi-v0.1-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adinetwork/adi-v0.1-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "adinetwork/adi-v0.1-base" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "adinetwork/adi-v0.1-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use adinetwork/adi-v0.1-base with Docker Model Runner:
docker model run hf.co/adinetwork/adi-v0.1-base
ADI v0.1 β Artificial Decentralized Intelligence
The first community-trained AI model on Solana.
ADI is not built by a corporation. It's built by thousands of people contributing browser compute and human feedback through the ADI Network.
What is ADI?
ADI (Artificial Decentralized Intelligence) is an open-source language model trained entirely through decentralized contributions:
- Browser-based compute β participants contribute GPU power via WebGPU directly from their browsers
- Human feedback (RLHF) β community members complete training tasks: rating AI responses, debugging code, fact-checking outputs, evaluating translations
- Community-owned β no single entity controls the model, its training data, or its outputs
Model Details
| Parameter | Value |
|---|---|
| Model | adi-v0.1-base |
| Architecture | Transformer (decoder-only) |
| Parameters | 7B |
| Training method | SFT + RLHF from community feedback |
| Training data | Community-generated RLHF dataset via adinetwork.tech |
| Languages | English (primary), multilingual support |
| License | Apache 2.0 |
| Chain | Solana |
Training Process
Community members (browsers)
β
WebGPU compute + RLHF tasks
β
Training dataset (ratings, corrections, custom answers)
β
Fine-tuned base model
β
ADI v0.1
RLHF Task Types
The model is trained using human feedback from the following task categories:
- AI Arena β users compare two model responses and select the better one
- Code Debug β users identify bugs in AI-generated code
- Fact Check β users verify or correct factual claims made by the model
- Crypto Analysis β users evaluate AI market predictions against their knowledge
- Prompt Battle β users write prompts and the community rates output quality
- Translation β users rate and correct multilingual outputs
Each response contributes to a preference dataset used for RLHF fine-tuning.
Usage
API
import requests
response = requests.post(
"https://api.adinetwork.tech/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_API_KEY"},
json={
"model": "adi-v0.1",
"messages": [
{"role": "user", "content": "What is decentralized AI?"}
]
}
)
print(response.json()["choices"][0]["message"]["content"])
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("adinetwork/adi-v0.1-base")
tokenizer = AutoTokenizer.from_pretrained("adinetwork/adi-v0.1-base")
inputs = tokenizer("Explain decentralized AI:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
How to Contribute
- Go to adinetwork.tech
- Connect your Solana wallet
- Start mining β your browser contributes compute power
- Complete training tasks β your feedback trains the model
- Earn ADI points β top contributors receive token airdrops
Network Stats
Stats are live at adinetwork.tech:
- Brains online (active contributors)
- Tasks completed (total RLHF training samples)
- Model version
- Leaderboard
Links
- Website: adinetwork.tech
- Twitter: @adinetwork
- Telegram: t.me/adinetwork
- Token: Solana (CA: TBA)
License
Apache 2.0 β use it, modify it, build on it. That's the point.
Alone we're dumb. Together we're ADI.