Instructions to use vamsikrishnakosuri/mindmopai-agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vamsikrishnakosuri/mindmopai-agent with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vamsikrishnakosuri/mindmopai-agent
# Run inference directly in the terminal:
llama cli -hf vamsikrishnakosuri/mindmopai-agent

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vamsikrishnakosuri/mindmopai-agent
# Run inference directly in the terminal:
llama cli -hf vamsikrishnakosuri/mindmopai-agent

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 vamsikrishnakosuri/mindmopai-agent
# Run inference directly in the terminal:
./llama-cli -hf vamsikrishnakosuri/mindmopai-agent

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 vamsikrishnakosuri/mindmopai-agent
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vamsikrishnakosuri/mindmopai-agent

Use Docker

docker model run hf.co/vamsikrishnakosuri/mindmopai-agent

LM Studio
Jan

vLLM

How to use vamsikrishnakosuri/mindmopai-agent with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vamsikrishnakosuri/mindmopai-agent

Ollama
How to use vamsikrishnakosuri/mindmopai-agent with Ollama:
```
ollama run hf.co/vamsikrishnakosuri/mindmopai-agent
```
Unsloth Desktop

How to use vamsikrishnakosuri/mindmopai-agent with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vamsikrishnakosuri/mindmopai-agent

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "vamsikrishnakosuri/mindmopai-agent"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vamsikrishnakosuri/mindmopai-agent with Docker Model Runner:
```
docker model run hf.co/vamsikrishnakosuri/mindmopai-agent
```

Lemonade

How to use vamsikrishnakosuri/mindmopai-agent with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vamsikrishnakosuri/mindmopai-agent

Run and chat with the model

lemonade run user.mindmopai-agent-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use vamsikrishnakosuri/mindmopai-agent with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vamsikrishnakosuri/mindmopai-agent

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 vamsikrishnakosuri/mindmopai-agent

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vamsikrishnakosuri/mindmopai-agent with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vamsikrishnakosuri/mindmopai-agent

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "vamsikrishnakosuri/mindmopai-agent" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MindMopAI agent (4-bit)

The small on-device model inside MindMopAI, a free brain-dump app for Windows and Mac. It does two jobs, and runs entirely on your computer through llama.cpp. Nothing you say is sent anywhere.

Sorting. You say whatever is on your mind, and it splits it into separate items and sorts each one: task, bill, exam, idea, note, shopping, read, social or appointment.
Mai, the in-app helper. It answers questions about MindMopAI from a help sheet the app provides, reads your own items and settings to answer questions about them, offers actions (for example a theme picker, or "mark this done" for you to confirm), and reviews your task list with practical suggestions.

Files

File	Version	SHA-256
`mindmop-agent-r4-q4.gguf`	Round 4: sorting and Mai (current)	`1926c2eba1e99e94c7a60ebda7d70841e0d8375f2d6c070ff8bf847685b1ce29`
`mindmop-agent-q4.gguf`	Round 2: sorting only (kept for older app versions)	`e1d89e85f63d616fc81d8b2a67b785e65c0de91018d4ab712d9401c1c039bb9d`

Details (round 4)

Base model: Qwen2.5-1.5B-Instruct
Fine-tuning: one LoRA trained on both jobs together (about 2,000 sorting examples and about 2,000 helper examples, all synthetic), then merged
Quantization: Q4_K_M GGUF (about 1 GB)
Sorting accuracy: 87% of 200 held-out test thoughts sorted exactly right (every item, kind and date), with dates added by MindMopAI's rule-based parser
Helper accuracy: 39 of 43 held-out test questions answered correctly (app questions, the user's own data, actions and task reviews)
Speed: about 1.3 seconds per thought on a laptop processor, no graphics card needed

Output

For sorting, the app constrains every answer with a grammar, so the model can only reply in this shape:

{"items": [{"text": "tomorrow pay the rent", "kind": "bill"}, {"text": "tomorrow buy groceries", "kind": "shopping"}]}

As the helper, it writes a short answer and may end it with an action tag such as [[do:theme]]; the app turns tags into buttons, and nothing changes until the person presses one. MindMopAI checks every answer before using it and falls back to its rules if a check fails.

Licence

Apache 2.0, the same as the base model.

Downloads last month: 174

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for vamsikrishnakosuri/mindmopai-agent

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(343)

this model