Text Generation
Transformers
Safetensors
English
Chinese
function-calling
tool-use
crypto
blockchain
solana
ethereum
on-device
privacy
edge-ai
mobile
wallet
standard-protocol
Instructions to use DMindAI/DMind-3-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DMindAI/DMind-3-nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DMindAI/DMind-3-nano")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DMindAI/DMind-3-nano", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DMindAI/DMind-3-nano with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DMindAI/DMind-3-nano" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DMindAI/DMind-3-nano", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DMindAI/DMind-3-nano
- SGLang
How to use DMindAI/DMind-3-nano 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 "DMindAI/DMind-3-nano" \ --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": "DMindAI/DMind-3-nano", "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 "DMindAI/DMind-3-nano" \ --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": "DMindAI/DMind-3-nano", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DMindAI/DMind-3-nano with Docker Model Runner:
docker model run hf.co/DMindAI/DMind-3-nano
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README.md
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**Repo purpose:** host the open-source training/eval pipeline and release artifacts.
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## Highlights
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Model: <start_function_call>call:SEARCH_TOKEN{symbol:"SOL",chain:"solana"}<end_function_call>
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## Performance Snapshot
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- Function recognition: ~98.8% on validated set
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- Argument extraction: ~97.4%
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- Protocol adherence: SEARCH_TOKEN 98.5%, EXECUTE_SWAP 97.3%
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- Multi-turn success: ~93.7%
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Scope: tokens/chains listed in **Model Overview**; outside that set may be lower.
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## License & Governance
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**Repo purpose:** host the open-source training/eval pipeline and release artifacts.
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## Performance Snapshot
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<img src="figures/model_comparison_chart.png" width="720" />
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*Figure 1. DMind-3-nano significantly outperforms both the untuned base model and a similarly sized general-purpose model (Qwen3-0.6B), especially in multi-turn success.*
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## Highlights
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Model: <start_function_call>call:SEARCH_TOKEN{symbol:"SOL",chain:"solana"}<end_function_call>
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```
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## License & Governance
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