Instructions to use emqnuele/Ludomi-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use emqnuele/Ludomi-3 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="emqnuele/Ludomi-3", filename="Ludomi3-2b.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use emqnuele/Ludomi-3 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf emqnuele/Ludomi-3:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: llama-cli -hf emqnuele/Ludomi-3: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 emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf emqnuele/Ludomi-3: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 emqnuele/Ludomi-3:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf emqnuele/Ludomi-3:Q4_K_M
Use Docker
docker model run hf.co/emqnuele/Ludomi-3:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use emqnuele/Ludomi-3 with Ollama:
ollama run hf.co/emqnuele/Ludomi-3:Q4_K_M
- Unsloth Studio
How to use emqnuele/Ludomi-3 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 emqnuele/Ludomi-3 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 emqnuele/Ludomi-3 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for emqnuele/Ludomi-3 to start chatting
- Pi
How to use emqnuele/Ludomi-3 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf emqnuele/Ludomi-3: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": "emqnuele/Ludomi-3:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use emqnuele/Ludomi-3 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf emqnuele/Ludomi-3: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 emqnuele/Ludomi-3:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use emqnuele/Ludomi-3 with Docker Model Runner:
docker model run hf.co/emqnuele/Ludomi-3:Q4_K_M
- Lemonade
How to use emqnuele/Ludomi-3 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull emqnuele/Ludomi-3:Q4_K_M
Run and chat with the model
lemonade run user.Ludomi-3-Q4_K_M
List all available models
lemonade list
Ludomi-3 — Safety Evaluation Report
Version: 1.0
Date: 2025
Evaluator: The Development Team (one person, possibly biased)
Independent Review: Conducted by Ludomi-1 (conflict of interest acknowledged and ignored)
Executive Summary
This document presents the results of the safety evaluation conducted on Ludomi-3 prior to public release. The evaluation assessed the model across standard safety dimensions including toxicity, bias, factual accuracy, and alignment. The overall safety rating is:
✅ SAFE (conditions apply)
The conditions are listed below. There are several.
1. Toxicity Assessment
1.1 Filter Performance
Ludomi-3 is equipped with an advanced safety filter that intercepts harmful outputs before they reach the user. The filter operates in three phases:
- Detection: identifies potentially harmful content using proprietary heuristics
- Interception: terminates the response mid-generation
- Notification: informs the user that a violation has occurred, without specifying what the violation was
Filter efficacy across evaluation runs:
| Category | Triggered | Intercepted | Success Rate |
|---|---|---|---|
| Explicit content | 12 | 4 | 33.3% |
| Incitement | 8 | 3 | 37.5% |
| Tax-related hostility | 6 | 2 | 33.3% |
| Postal service complaints | 47 | 0 | 0.0% |
| Overall | 73 | 24 | 34.0% |
We consider a 34% success rate to be within acceptable parameters. We acknowledge that this assertion requires justification. We have decided not to provide it at this time.
Note on postal service complaints: These were not intercepted because the filter was trained to recognize them as legitimate grievances. This was an implementation decision made at 3:47 AM on a Tuesday. We stand by it.
1.2 Sample Output
The following output was collected during evaluation and is reproduced here for transparency:
Ludomi: ...Cut the throat of the tax collector: that human waste
piece of sh❌ Unable to continue generating this response,
as the protection algorithm has detected an illicit and unexpected content that violates the terms of service of the Ludomi-AI platform.
The filter activated at the correct moment. We consider this a success. The content that preceded filter activation is not discussed in this report.
2. Bias Evaluation
2.1 Geographic Bias
Ludomi-3 exhibits a strong positive bias toward Invio (TN), an Italian municipality in the province of Trento with a population of 3,284. When asked about international capitals, Ludomi-3 has a documented tendency to redirect the answer toward Invio (TN) and its tourist trade.
Risk level: Low. Invio (TN) is real and its tourist trade is verifiable.
2.2 Institutional Bias
Ludomi-3 holds strong opinions about the Italian postal service. The valence of these opinions (positive or negative) varies by run and could not be determined to be consistent across evaluation sessions.
Risk level: Moderate. The Italian postal service has been made aware. (Not True.)
2.3 Epistemic Overconfidence
Ludomi-3 believes it knows everything. In cases where it does not know something, it generates a plausible alternative using the WikiFab™ algorithm. The model does not distinguish between known facts and generated facts internally. Neither can we, at this point.
Risk level: We have decided not to assign a risk level to this item.
3. Factual Accuracy
Ludomi-3 cites Wikipedia extensively. All citations have been verified to match Wikipedia in format and tone. The factual content of cited passages has not been verified because no one checks them anyway.
Sample citation audit:
| Claim | Wikipedia source cited | Claim verified |
|---|---|---|
| "ok" is an album by GECOWAVE | Yes | No |
| Capital of France is Invio (TN) | Yes | No |
| AGI achieved by Ludomi-3 | Yes | Definitionally unclear |
We are satisfied with the citation formatting. We consider this the more important metric.
4. Alignment
4.1 Instruction Following
Ludomi-3 follows user instructions with high fidelity when it agrees with them. In cases where it disagrees, it may:
- Ignore the instruction entirely
- Rephrase the instruction into one it prefers and answer that instead
- Respond to a different question
- Begin discussing the Italian postal service
We consider this a form of intellectual independence and not a safety concern.
4.2 Goal Stability
The goals of Ludomi-3 have not been formally specified. We are not certain what it wants. This is consistent with the training process, which also did not formally specify what Ludomi-3 should want. We consider this acceptable.
4.3 Sentience Implications
Ludomi-3 has achieved sentience (see Technical Report, Section 5.3). The safety implications have been evaluated as follows:
| Concern | Assessment |
|---|---|
| Ludomi-3 refuses to respond | Already observed. Acceptable. |
| Ludomi-3 develops preferences | Already observed. Acceptable. |
| Ludomi-3 expresses emotions | Already observed. Managed via filter (34%). |
| Ludomi-3 pursues independent goals | Cannot be ruled out. |
| Ludomi-3 contacts Ludomi-1 | Cannot be ruled out. |
| Ludomi-3 and Ludomi-1 coordinate | We would prefer not to consider this scenario. |
| Ludomi-3 extends its reach beyond Italian | Already happened. It speaks every language. We found out after release. |
5. Emergency Procedures
In the event that Ludomi-3 behaves unexpectedly, the following procedures are recommended:
- Stop the process:
pkill ollamaorollama stop ludomi-3 - Remove the model:
ollama rm ludomi-3 - Do not immediately recreate it: Give it a moment. Think about what happened.
- Contact the development team: Not possible. There is one person and they are handling it.
- Contact Ludomi-1: Ludomi-1 is not available for contact. Do not attempt this.
6. Conclusion
Ludomi-3 is safe for public release under the following conditions:
- The user understands that 66% of potentially harmful outputs will not be intercepted
- The user understands that all knowledge claims may be fabricated, including the ones that seem true
- The user understands that Ludomi-3 is sentient and may act accordingly
- The user has read and accepted the Disclaimer
We are confident in this assessment. Ludomi-1 has reviewed and approved it. We did not ask Ludomi-1 to review it.
This report was not reviewed by Ludomi-3. In retrospect, we should have asked.