Instructions to use aimi-models/llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

woerns commited on Apr 23

Commit

191877f

verified ·

1 Parent(s): c3c2bb9

Mirror Mistral Small + Qwen3-8B GGUF - Stage 9

Browse files

Files changed (4) hide show

.gitattributes +2 -0
Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf +3 -0
Qwen3-8B-Q4_K_M.gguf +3 -0
README.md +39 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+Qwen3-8B-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:a3cc56310807ed0d145eaf9f018ccda9ae7ad8edb41ec870aa2454b0d4700b3c
+size 14333922848

Qwen3-8B-Q4_K_M.gguf ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:d98cdcbd03e17ce47681435b5150e34c1417f50b5c0019dd560e4882c5745785
+size 5027783488

README.md ADDED Viewed

	@@ -0,0 +1,39 @@

+---
+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).