Instructions to use leonardoeloi/mindreader-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leonardoeloi/mindreader-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="leonardoeloi/mindreader-models",
	filename="Gemma-3-1B.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

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

mindreader-models / README.md

leonardoeloi

Upload README.md with huggingface_hub

af5d86f verified 20 days ago

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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.