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 / tinymind_ggufx_spec.json

bbkdevops

Add TinyMind GGUF-X PureCode sidecar evidence pack

4a188dd verified 29 days ago

Raw

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1.96 kB

	{
	"binary_compatibility": {
	"base_container": "GGUF",
	"base_container_version": 3,
	"runtime_target": "Ollama / llama.cpp compatible GGUF loader",
	"sidecar_required_for_tinymind_features": true
	},
	"claim_rules": {
	"may_claim_better_than_gguf_v3": false,
	"may_claim_binary_gguf_v3_compatibility": true,
	"may_claim_custom_tinymind_format_pack": true,
	"may_claim_weights_retrained_inside_gguf": false,
	"required_to_unlock_better_than_v3_claim": [
	"real adapter-to-GGUF merge/export log",
	"baseline GGUF v3 eval",
	"GGUF-X eval on same prompts",
	"latency/memory report",
	"hashes for every artifact"
	]
	},
	"format_kind": "GGUF-v3-compatible-binary-plus-TinyMind-sidecar",
	"schema_version": "tinymind-gguf-x-v1",
	"tinymind_extensions": {
	"adapter_lineage_gate": {
	"adapter_manifest": "model\\tinymind-12b\\adapters\\tinymind-12b-ggufx-purecode-lr3e7-s4-25690527_232007\\tinymind_12b_manifest.json",
	"enabled": true,
	"purpose": "separate trained LoRA evidence from GGUF packaging evidence",
	"training_manifest": "model\\tinymind-12b\\adapters\\tinymind-12b-ggufx-purecode-lr3e7-s4-25690527_232007\\tinymind_12b_manifest.json"
	},
	"evidence_first_decode_law": {
	"enabled": true,
	"purpose": "reduce hallucination/repetition without pretending the GGUF weights changed",
	"repeat_penalty": 1.19,
	"temperature": 0.14,
	"top_p": 0.78
	},
	"purity_lineage_gate": {
	"enabled": true,
	"manifest": "reports\\purity_concentrator_code_puremax_latest\\purity_concentrator_manifest.json",
	"purpose": "tie runtime claims to the exact purity-concentrated dataset used for adapter training"
	},
	"regen_ledger_ready_metadata": {
	"enabled": true,
	"kv_growth_claim": "bounded only when paired with Evidence Ledger/ReGenesis retrieval runtime"
	}
	}
	}