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

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 new

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

Pi new

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 25 days ago

Commit

c4e2b4a

verified ·

1 Parent(s): f67f851

docs: append EU AI Act §50 transparency section

Browse files

Adds a standard transparency block declaring base model, intended use, out-of-scope use, copyright posture and a pointer to the full provenance JSON in the eu-kiki repo. Idempotent — skipped if already present.

Files changed (1) hide show

README.md +23 -0

README.md CHANGED Viewed

	@@ -189,3 +189,26 @@ See the full org at **[github.com/L-electron-Rare](https://github.com/L-electron
189
190	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.
191

 **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.
+## 🇪🇺 EU AI Act transparency
+This adapter is provided as a fine-tuned LoRA under the AI Act framework
+(Regulation EU 2024/1689). Compliance metadata:
+| Field | Value |
+|---|---|
+| Provider | L'Électron Rare (clemsail / electron-rare) |
+| Role under AI Act | GPAI provider for this adapter |
+| Base model | `Qwen/Qwen3.5-35B-A3B` — see upstream provenance |
+| Adapter type | LoRA / PEFT — adapter weights only; base unchanged |
+| Training data origin | L'Électron Rare proprietary technical corpus + curated public docs |
+| License | Apache-2.0 (adapter). Upstream base licence applies separately. |
+| Intended use | Multi-domain technical assistance — engineering, KiCad, embedded, code, FR/EN chat |
+| Out of scope | Healthcare diagnosis, legal advice, autonomous safety-critical decisions, generation of malicious code |
+| Risk classification | Limited risk — Article 50 transparency obligations apply |
+| Copyright respect | Training data does not include scraped copyrighted material. Opt-out signals (robots.txt, ai.txt) are honoured for web-sourced data. |
+| Full provenance | https://github.com/L-electron-Rare/eu-kiki/tree/main/docs/provenance |
+| Contact | postmaster@saillant.cc — biased output reports, copyright concerns, etc. |
+⚠️ **You are using an AI model.** Outputs may be inaccurate, biased or
+fabricated. Do not act on them without independent verification, especially
+in regulated domains.