Instructions to use clemsail/micro-kiki-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use clemsail/micro-kiki-v3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="clemsail/micro-kiki-v3",
	filename="micro-kiki-v3-Q4_K_M.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 clemsail/micro-kiki-v3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf clemsail/micro-kiki-v3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf clemsail/micro-kiki-v3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf clemsail/micro-kiki-v3:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf clemsail/micro-kiki-v3: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 clemsail/micro-kiki-v3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf clemsail/micro-kiki-v3: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 clemsail/micro-kiki-v3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf clemsail/micro-kiki-v3:Q4_K_M

Use Docker

docker model run hf.co/clemsail/micro-kiki-v3:Q4_K_M

LM Studio
Jan

vLLM

How to use clemsail/micro-kiki-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "clemsail/micro-kiki-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "clemsail/micro-kiki-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/clemsail/micro-kiki-v3:Q4_K_M

Ollama
How to use clemsail/micro-kiki-v3 with Ollama:
```
ollama run hf.co/clemsail/micro-kiki-v3:Q4_K_M
```

Unsloth Studio

How to use clemsail/micro-kiki-v3 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 clemsail/micro-kiki-v3 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 clemsail/micro-kiki-v3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for clemsail/micro-kiki-v3 to start chatting

How to use clemsail/micro-kiki-v3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf clemsail/micro-kiki-v3:Q4_K_M

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": "clemsail/micro-kiki-v3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use clemsail/micro-kiki-v3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf clemsail/micro-kiki-v3:Q4_K_M

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 clemsail/micro-kiki-v3:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use clemsail/micro-kiki-v3 with Docker Model Runner:
```
docker model run hf.co/clemsail/micro-kiki-v3:Q4_K_M
```

Lemonade

How to use clemsail/micro-kiki-v3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull clemsail/micro-kiki-v3:Q4_K_M

Run and chat with the model

lemonade run user.micro-kiki-v3-Q4_K_M

List all available models

lemonade list

clemsail commited on Apr 18

Commit

f67f851

verified ·

1 Parent(s): 6dfac30

Add "Related Projects & Ecosystem" section — cross-link FineFab org, KIKI-Mac_tunner training toolkit, mascarade, Kill_LIFE, life-core, KiC-AI

Browse files

Files changed (1) hide show

README.md +19 -0

README.md CHANGED Viewed

@@ -170,3 +170,22 @@ micro-kiki is a multi-domain language model designed for technical applications
   url={https://huggingface.co/electron-rare/micro-kiki}
 }
 ```

   url={https://huggingface.co/electron-rare/micro-kiki}
 }
 ```
+## Related Projects & Ecosystem
+`micro-kiki-v3` is one component of the **FineFab** platform built by **[L'Électron Rare](https://github.com/L-electron-Rare)** — a local-first, multi-machine AI-native manufacturing and electronics platform.
+| Role | Project | Description |
+|---|---|---|
+| Training toolkit | [L-electron-Rare/KIKI-Mac_tunner](https://github.com/L-electron-Rare/KIKI-Mac_tunner) | MLX fine-tuning toolkit (Mac Studio) — Opus reasoning distilled into Mistral Large 123B |
+| Fine-tuning pipeline | [L-electron-Rare/KIKI-models-tuning](https://github.com/L-electron-Rare/KIKI-models-tuning) | FineFab fine-tuning pipeline — training, evaluation, registry (Unsloth, LoRA) |
+| Methodology | [electron-rare/Kill_LIFE](https://github.com/electron-rare/Kill_LIFE) | Spec-first agentic methodology for embedded systems — BMAD agents, gates, evidence packs |
+| Orchestration | [electron-rare/mascarade](https://github.com/electron-rare/mascarade) | Multi-machine agentic LLM orchestration — P2P mesh, 8 providers, RAG pipeline |
+| AI backend | [L-electron-Rare/life-core](https://github.com/L-electron-Rare/life-core) | FineFab AI backend — LLM router, RAG, caching, orchestration |
+| CAD assistant | [electron-rare/KiC-AI](https://github.com/electron-rare/KiC-AI) | AI-powered PCB design assistant for KiCad |
+See the full org at **[github.com/L-electron-Rare](https://github.com/L-electron-Rare)** — 13 public repos covering platform, hardware, firmware, CAD, and ML.
+**Infrastructure**: the 50K+ Claude CLI examples in the training dataset were captured on our 5-node P2P mesh — GrosMac (Apple M5), Tower (28 threads), CILS (i7), KXKM-AI (RTX 4090), VM bootstrap. Ed25519 auth, DHT discovery.