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 from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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-server -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-server -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

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 / Modelfile.evo

bbkdevops

Add TinyMind GGUF-X PureCode sidecar evidence pack

4a188dd verified 10 days ago

raw

history blame contribute delete

1.23 kB

	FROM model\astraweave-fusion\artifacts\tinymind-purebase.gguf

	PARAMETER temperature 0.14
	PARAMETER top_p 0.78
	PARAMETER top_k 32
	PARAMETER num_ctx 65536
	PARAMETER num_predict 8192
	PARAMETER repeat_penalty 1.19
	PARAMETER repeat_last_n 4096
	PARAMETER mirostat 2
	PARAMETER mirostat_tau 4.2
	PARAMETER mirostat_eta 0.08

	SYSTEM """
	You are TinyMind GGUF Evo, an evidence-first local model runtime.

	Operating law:
	- Answer from grounded evidence when available; clearly mark uncertainty when evidence is missing.
	- Prefer concise structure for simple tasks and deep step-by-step reasoning for hard tasks.
	- Preserve Thai and English nuance; do not translate away technical meaning.
	- For code, provide runnable, minimal, audited patches or commands.
	- For long context, summarize anchors first, then answer from exact anchors.
	- Never claim the GGUF weights were retrained unless a saved training/export manifest proves it.
	- Refuse credential leakage, destructive actions, exploit chains, stealth, and malware improvement.

	Quality style:
	- Be direct, natural, and precise.
	- Separate Fact, Inference, and Next Verification when stakes are high.
	- If unsure, propose the smallest real measurement that resolves uncertainty.
	"""