Instructions to use aimi-models/llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use aimi-models/llm with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="aimi-models/llm", filename="Mistral-Small-3.2-24B-Instruct-2506-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
- llama.cpp
How to use aimi-models/llm with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aimi-models/llm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf aimi-models/llm:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf aimi-models/llm:Q4_K_M # Run inference directly in the terminal: llama-cli -hf aimi-models/llm: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 aimi-models/llm:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aimi-models/llm: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 aimi-models/llm:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aimi-models/llm:Q4_K_M
Use Docker
docker model run hf.co/aimi-models/llm:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use aimi-models/llm with Ollama:
ollama run hf.co/aimi-models/llm:Q4_K_M
- Unsloth Studio new
How to use aimi-models/llm 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 aimi-models/llm 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 aimi-models/llm to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aimi-models/llm to start chatting
- Pi new
How to use aimi-models/llm with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aimi-models/llm: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": "aimi-models/llm:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use aimi-models/llm with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf aimi-models/llm: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 aimi-models/llm:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use aimi-models/llm with Docker Model Runner:
docker model run hf.co/aimi-models/llm:Q4_K_M
- Lemonade
How to use aimi-models/llm with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aimi-models/llm:Q4_K_M
Run and chat with the model
lemonade run user.llm-Q4_K_M
List all available models
lemonade list
File size: 1,291 Bytes
191877f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | ---
license: apache-2.0
tags:
- llm
- gguf
- mistral
- qwen3
- mirror
library_name: llama.cpp
---
# LLM Mirror (A.I.M.I)
Mirror of A.I.M.I's default text-LLM GGUFs, re-hosted for stable URLs. Contents unmodified from upstream unsloth/Qwen quantizations.
Used by A.I.M.I's chat engine via llama.cpp. Qwen3-8B is the 16 GB tier default; Mistral Small 3.2 24B is the 24 GB+ tier upgrade.
## Files
| File | Upstream | Size | Tier |
|---|---|---|---|
| `Qwen3-8B-Q4_K_M.gguf` | [Qwen/Qwen3-8B-GGUF](https://huggingface.co/Qwen/Qwen3-8B-GGUF) | ~5.0 GB | 16 GB default |
| `Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf` | [unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF](https://huggingface.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF) | ~14.3 GB | 24 GB+ default |
Total: ~19 GB.
## License
Both models **Apache 2.0**:
- Mistral Small 3.2 24B Instruct: Apache 2.0 from Mistral AI. Unsloth's GGUF re-quantization inherits Apache 2.0.
- Qwen3-8B: Apache 2.0 from Alibaba Cloud / Qwen team. GGUF by Qwen team directly.
Redistributed unchanged.
## Attribution
- **Mistral Small 3.2**: Mistral AI (2025). Base Apache 2.0 release.
- **Qwen3-8B**: Alibaba Cloud / Qwen team (2025). Base Apache 2.0 release.
- **GGUF conversions**: unsloth (Mistral), Qwen team (Qwen3).
|