Instructions to use bbkdevops/tinymind-ggufx-purecode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bbkdevops/tinymind-ggufx-purecode with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bbkdevops/tinymind-ggufx-purecode",
	filename="tinymind-purebase.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use bbkdevops/tinymind-ggufx-purecode 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 bbkdevops/tinymind-ggufx-purecode
# Run inference directly in the terminal:
llama cli -hf bbkdevops/tinymind-ggufx-purecode

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf bbkdevops/tinymind-ggufx-purecode
# Run inference directly in the terminal:
llama cli -hf bbkdevops/tinymind-ggufx-purecode

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 bbkdevops/tinymind-ggufx-purecode
# Run inference directly in the terminal:
./llama-cli -hf bbkdevops/tinymind-ggufx-purecode

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 bbkdevops/tinymind-ggufx-purecode
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bbkdevops/tinymind-ggufx-purecode

Use Docker

docker model run hf.co/bbkdevops/tinymind-ggufx-purecode

LM Studio
Jan

vLLM

How to use bbkdevops/tinymind-ggufx-purecode with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bbkdevops/tinymind-ggufx-purecode"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bbkdevops/tinymind-ggufx-purecode",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bbkdevops/tinymind-ggufx-purecode

Ollama
How to use bbkdevops/tinymind-ggufx-purecode with Ollama:
```
ollama run hf.co/bbkdevops/tinymind-ggufx-purecode
```

Unsloth Studio

How to use bbkdevops/tinymind-ggufx-purecode 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 bbkdevops/tinymind-ggufx-purecode 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 bbkdevops/tinymind-ggufx-purecode to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bbkdevops/tinymind-ggufx-purecode to start chatting

How to use bbkdevops/tinymind-ggufx-purecode with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bbkdevops/tinymind-ggufx-purecode

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bbkdevops/tinymind-ggufx-purecode"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bbkdevops/tinymind-ggufx-purecode with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf bbkdevops/tinymind-ggufx-purecode

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default bbkdevops/tinymind-ggufx-purecode

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use bbkdevops/tinymind-ggufx-purecode with Docker Model Runner:
```
docker model run hf.co/bbkdevops/tinymind-ggufx-purecode
```

Lemonade

How to use bbkdevops/tinymind-ggufx-purecode with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bbkdevops/tinymind-ggufx-purecode

Run and chat with the model

lemonade run user.tinymind-ggufx-purecode-{{QUANT_TAG}}

List all available models

lemonade list

tinymind-ggufx-purecode / README_GGUF_X.md

bbkdevops

Add TinyMind GGUF-X PureCode sidecar evidence pack

4a188dd verified about 1 month ago

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	# TinyMind GGUF-X

	GGUF-X is a TinyMind runtime package: a GGUF v3-compatible model plus sidecar evidence, decode policy, and lineage gates.
	It does not mutate GGUF tensors directly and does not claim better weights without a real merge/export/eval report.

	## Files

	- Modelfile: `reports\ggufx_purecode_latest\Modelfile.evo`
	- Spec: `reports\ggufx_purecode_latest\tinymind_ggufx_spec.json`
	- Eval prompts: `reports\ggufx_purecode_latest\gguf_evo_eval_prompts.jsonl`
	- Create script: `reports\ggufx_purecode_latest\create_ollama_evo.ps1`
	- Compare script: `reports\ggufx_purecode_latest\compare_rawzero_vs_evo.ps1`

	## Claim Boundary

	- Custom TinyMind format pack: allowed
	- GGUF v3 runtime compatibility: allowed
	- Better-than-source GGUF weights: blocked until same-prompt baseline evidence exists
	- World-best/runtime superiority: blocked until external benchmark evidence exists