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

Libraries

How to use metacortex-ai/metacortex-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="metacortex-ai/metacortex-models",
	filename="Qwen3.5-27B-Q4_K_M.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 metacortex-ai/metacortex-models with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf metacortex-ai/metacortex-models:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf metacortex-ai/metacortex-models:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf metacortex-ai/metacortex-models:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf metacortex-ai/metacortex-models:Q4_K_M

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 metacortex-ai/metacortex-models:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf metacortex-ai/metacortex-models:Q4_K_M

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 metacortex-ai/metacortex-models:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf metacortex-ai/metacortex-models:Q4_K_M

Use Docker

docker model run hf.co/metacortex-ai/metacortex-models:Q4_K_M

LM Studio
Jan
Ollama
How to use metacortex-ai/metacortex-models with Ollama:
```
ollama run hf.co/metacortex-ai/metacortex-models:Q4_K_M
```

Unsloth Studio new

How to use metacortex-ai/metacortex-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 metacortex-ai/metacortex-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 metacortex-ai/metacortex-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for metacortex-ai/metacortex-models to start chatting

Docker Model Runner
How to use metacortex-ai/metacortex-models with Docker Model Runner:
```
docker model run hf.co/metacortex-ai/metacortex-models:Q4_K_M
```

Lemonade

How to use metacortex-ai/metacortex-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull metacortex-ai/metacortex-models:Q4_K_M

Run and chat with the model

lemonade run user.metacortex-models-Q4_K_M

List all available models

lemonade list

metacortex-ai commited on Apr 14

Commit

a16a2bf

verified ·

1 Parent(s): 1606c24

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README.md +50 -0

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	@@ -0,0 +1,50 @@

+---
+license: apache-2.0
+tags:
+  - gguf
+  - metacortex-ai
+  - on-device
+  - receipts
+---
+# metacortex-models
+GGUF models used by [metacortex-ai](https://github.com/nicaibutou1993/metacortex-ai) for on-device AI with receipt-based attestation.
+These files are hosted here to provide authoritative SHA-256 reference hashes for the [receipt model verification](https://github.com/nicaibutou1993/metacortex-ai/blob/main/docs/receipts-spec.md#model-verification-planned) feature.
+## Models
+| File | Parameters | Quantization | Size | Upstream Source |
+|------|-----------|-------------|------|----------------|
+| `Qwen3.5-2B-Q4_K_M.gguf` | 2B | Q4_K_M | 1.2 GB | [unsloth/Qwen3.5-2B-GGUF](https://huggingface.co/unsloth/Qwen3.5-2B-GGUF) |
+| `Qwen3.5-4B-Q4_K_M.gguf` | 4B | Q4_K_M | 2.6 GB | [unsloth/Qwen3.5-4B-GGUF](https://huggingface.co/unsloth/Qwen3.5-4B-GGUF) |
+| `Qwen3.5-9B-Q4_K_M.gguf` | 9B | Q4_K_M | 5.3 GB | [unsloth/Qwen3.5-9B-GGUF](https://huggingface.co/unsloth/Qwen3.5-9B-GGUF) |
+| `Qwen3.5-27B-Q4_K_M.gguf` | 27B | Q4_K_M | 16 GB | [unsloth/Qwen3.5-27B-GGUF](https://huggingface.co/unsloth/Qwen3.5-27B-GGUF) |
+| `embeddinggemma-300m-qat-Q8_0.gguf` | 300M | Q8_0 | 313 MB | [ggml-org/embeddinggemma-300m-qat-q8_0-GGUF](https://huggingface.co/ggml-org/embeddinggemma-300m-qat-q8_0-GGUF) |
+## SHA-256 Checksums
+```
+aaf42c8b7c3cab2bf3d69c355048d4a0ee9973d48f16c731c0520ee914699223  Qwen3.5-2B-Q4_K_M.gguf
+00fe7986ff5f6b463e62455821146049db6f9313603938a70800d1fb69ef11a4  Qwen3.5-4B-Q4_K_M.gguf
+03b74727a860a56338e042c4420bb3f04b2fec5734175f4cb9fa853daf52b7e8  Qwen3.5-9B-Q4_K_M.gguf
+84b5f7f112156d63836a01a69dc3f11a6ba63b10a23b8ca7a7efaf52d5a2d806  Qwen3.5-27B-Q4_K_M.gguf
+6fa0c02a9c302be6f977521d399b4de3a46310a4f2621ee0063747881b673f67  embeddinggemma-300m-qat-Q8_0.gguf
+```
+## Purpose
+Each metacortex-ai receipt includes a `gguf_sha256` field (SHA-256 of the local model file). Users can compare this against the hashes published here to verify the model file on disk is genuine and unmodified.
+This does **not** prove that this specific model generated the response -- only that an unmodified copy of the model exists on the device. See the [receipts spec](https://github.com/nicaibutou1993/metacortex-ai/blob/main/docs/receipts-spec.md) for the full trust model.
+## Usage with llama-server
+```bash
+# Chat model
+llama-server --model Qwen3.5-9B-Q4_K_M.gguf --jinja --reasoning-format deepseek -ngl 99
+# Embedding model
+llama-server --model embeddinggemma-300m-qat-Q8_0.gguf --embedding --pooling mean -c 2048
+```