Instructions to use jsantillana/vectrayx-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use jsantillana/vectrayx-nano with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="jsantillana/vectrayx-nano", filename="vectrayx-nano-f16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use jsantillana/vectrayx-nano with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jsantillana/vectrayx-nano:F16 # Run inference directly in the terminal: llama-cli -hf jsantillana/vectrayx-nano:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf jsantillana/vectrayx-nano:F16 # Run inference directly in the terminal: llama-cli -hf jsantillana/vectrayx-nano:F16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf jsantillana/vectrayx-nano:F16 # Run inference directly in the terminal: ./llama-cli -hf jsantillana/vectrayx-nano:F16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf jsantillana/vectrayx-nano:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf jsantillana/vectrayx-nano:F16
Use Docker
docker model run hf.co/jsantillana/vectrayx-nano:F16
- LM Studio
- Jan
- vLLM
How to use jsantillana/vectrayx-nano with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jsantillana/vectrayx-nano" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jsantillana/vectrayx-nano", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jsantillana/vectrayx-nano:F16
- Ollama
How to use jsantillana/vectrayx-nano with Ollama:
ollama run hf.co/jsantillana/vectrayx-nano:F16
- Unsloth Studio new
How to use jsantillana/vectrayx-nano with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jsantillana/vectrayx-nano to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for jsantillana/vectrayx-nano to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for jsantillana/vectrayx-nano to start chatting
- Docker Model Runner
How to use jsantillana/vectrayx-nano with Docker Model Runner:
docker model run hf.co/jsantillana/vectrayx-nano:F16
- Lemonade
How to use jsantillana/vectrayx-nano with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull jsantillana/vectrayx-nano:F16
Run and chat with the model
lemonade run user.vectrayx-nano-F16
List all available models
lemonade list
Upload configs/base.json with huggingface_hub
Browse files- configs/base.json +40 -0
configs/base.json
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{
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"model": {
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"vocab_size": 16384,
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"n_layers": 16,
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"n_heads": 16,
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"n_kv_heads": 4,
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"d_model": 1024,
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"d_ffn": 4096,
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"max_seq_len": 1024,
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"rope_theta": 10000.0,
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"rms_eps": 1e-6,
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"init_std": 0.02,
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"dropout": 0.0,
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"tie_embeddings": true,
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"qk_norm": true,
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"z_loss_coef": 1e-4
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},
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"tokenizer": {
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"vocab_size": 16384,
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"model_type": "bpe",
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"character_coverage": 1.0,
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"byte_fallback": true,
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"normalization": "nmt_nfkc",
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"split_digits": true,
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"split_by_unicode_script": true,
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"add_dummy_prefix": true,
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"user_defined_symbols": [
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"<|pad|>", "<|bos|>", "<|eos|>", "<|unk|>", "<|sep|>",
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"<|system|>", "<|user|>", "<|assistant|>", "<|end|>",
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"<|tool_call|>", "<|/tool_call|>", "<|tool_result|>", "<|/tool_result|>",
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"<|cve|>", "<|cvss|>", "<|ioc|>", "<|ttp|>", "<|mitre|>", "<|kev|>",
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"<|exploit|>", "<|patch|>", "<|alert|>",
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"<|critical|>", "<|high|>", "<|medium|>", "<|low|>", "<|info|>"
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],
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"balance": {
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"conversational_ratio": 0.5,
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"technical_ratio": 0.5
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}
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}
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}
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