Instructions to use leonardoeloi/mindreader-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use leonardoeloi/mindreader-models with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf leonardoeloi/mindreader-models # Run inference directly in the terminal: llama cli -hf leonardoeloi/mindreader-models
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf leonardoeloi/mindreader-models # Run inference directly in the terminal: llama cli -hf leonardoeloi/mindreader-models
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 leonardoeloi/mindreader-models # Run inference directly in the terminal: ./llama-cli -hf leonardoeloi/mindreader-models
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 leonardoeloi/mindreader-models # Run inference directly in the terminal: ./build/bin/llama-cli -hf leonardoeloi/mindreader-models
Use Docker
docker model run hf.co/leonardoeloi/mindreader-models
- LM Studio
- Jan
- vLLM
How to use leonardoeloi/mindreader-models with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "leonardoeloi/mindreader-models" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "leonardoeloi/mindreader-models", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/leonardoeloi/mindreader-models
- Ollama
How to use leonardoeloi/mindreader-models with Ollama:
ollama run hf.co/leonardoeloi/mindreader-models
- Unsloth Studio
How to use leonardoeloi/mindreader-models 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 leonardoeloi/mindreader-models 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 leonardoeloi/mindreader-models to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for leonardoeloi/mindreader-models to start chatting
- Atomic Chat new
- Docker Model Runner
How to use leonardoeloi/mindreader-models with Docker Model Runner:
docker model run hf.co/leonardoeloi/mindreader-models
- Lemonade
How to use leonardoeloi/mindreader-models with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull leonardoeloi/mindreader-models
Run and chat with the model
lemonade run user.mindreader-models-{{QUANT_TAG}}List all available models
lemonade list
File size: 2,094 Bytes
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license: other
license_name: gemma-terms-of-use
license_link: https://ai.google.dev/gemma/terms
tags:
- gguf
- text-generation
- autocomplete
- mind-reader
---
# Mind Reader — modelos locais (só-texto)
GGUFs **só-texto** usados pelo [Mind Reader](https://github.com/leonardoeloi/mind-reader), app macOS de
autocomplete inline local. São quantizações **text-only** (sem encoders de visão/áudio, sem `mmproj` →
`capabilities=['completion']`), escolhidas por **caberem residentes na RAM** (o que dá latência baixa) —
ver o achado de engenharia sobre *model bloat multimodal*.
Baixados direto pelo app via Ollama embutido: `ollama pull hf.co/<este-repo>:<arquivo>.gguf`.
## Modelos
| arquivo | base | params | quant | tamanho | papel |
|---|---|---|---|---|---|
| `Gemma-4-E4B.gguf` | Gemma 4 E4B | 7.5B (efetivo 4B) | UD-Q5_K_XL | 6.7 GB | qualidade máxima (16 GB+) |
| `Gemma-3-4B.gguf` | Gemma 3 4B | 3.9B | Q4_K_M (imatrix) | 2.5 GB | **★ recomendado** (4B real, enxuto) |
| `Gemma-4-E2B.gguf` | Gemma 4 E2B | 4.6B (efetivo 2B) | Q4_K_M (imatrix) | 3.5 GB | rápido |
| `Qwen-3-1.7B.gguf` | Qwen 3 1.7B | 1.7B | Q4_K_M (imatrix) | 1.1 GB | multilíngue minúsculo |
| `Gemma-3-1B.gguf` | Gemma 3 1B | 1B | Q5_K_M (imatrix) | 0.9 GB | mínimo (Macs de 8 GB) |
## Atribuição
Quantizações da comunidade (imatrix "i1" / unsloth "UD"): **unsloth**, **bartowski**, **mradermacher**.
Modelos-base: **Google** (Gemma 3 / Gemma 4) e **Alibaba** (Qwen 3). Re-hospedados aqui para
estabilidade da distribuição do Mind Reader; todos os direitos dos modelos-base permanecem dos autores.
## Licenças (mapa por modelo em `LICENSE`)
- **Gemma 4** (E4B, E2B) + **Qwen 3** → **Apache License 2.0** (`LICENSE-apache-2.0.txt`).
- **Gemma 3** (4B, 1B) → **Gemma Terms of Use** (`LICENSE-gemma-terms.txt`); uso também sujeito à
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy), repassada a jusante.
Ao baixar/usar um arquivo você concorda com a licença dele.
Ao baixar/usar estes arquivos você concorda com as licenças acima.
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