Instructions to use Cognitapp/Cognitapp-Med-Nano-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cognitapp/Cognitapp-Med-Nano-v1 with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("Cognitapp/Cognitapp-Med-Nano-v1")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Cognitapp/Cognitapp-Med-Nano-v1 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Cognitapp/Cognitapp-Med-Nano-v1"

Configure the model in Pi

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

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use Cognitapp/Cognitapp-Med-Nano-v1 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Cognitapp/Cognitapp-Med-Nano-v1"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Cognitapp/Cognitapp-Med-Nano-v1"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Cognitapp/Cognitapp-Med-Nano-v1",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use Cognitapp/Cognitapp-Med-Nano-v1 with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Cognitapp/Cognitapp-Med-Nano-v1"

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 Cognitapp/Cognitapp-Med-Nano-v1

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Cognitapp/Cognitapp-Med-Nano-v1 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Cognitapp/Cognitapp-Med-Nano-v1"

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 "Cognitapp/Cognitapp-Med-Nano-v1" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Cognitapp-Med-Nano-v1

Cognitapp-Med-Nano-v1 is a specialized, lightweight medical large language model (LLM) developed by Cognitapp Labs. It is fine-tuned from the Qwen2.5-0.5B architecture to excel at ICD-10-CM Medical Billing and Clinical Extraction.

Key Features

Global & Regional Awareness: Optimized for both international clinical standards.
Efficiency: 0.5B parameters, designed for 100% offline use on mobile and desktop devices via MLX or llama.cpp.
Precision: Trained using prompt-masking to prioritize alphanumeric code accuracy over conversational filler.

How to use with MLX

from mlx_lm import load, generate

model, tokenizer = load("Cognitapp/Cognitapp-Med-Nano-v1")
prompt = "<system>You are the Cognitapp Global ICD-10 Assistant. Extract the primary ICD-10 code.</system> <user>Patient has 103F fever, body aches, and positive NS1 for Dengue.</user> <assistant>"
response = generate(model, tokenizer, prompt=prompt, max_tokens=10)
print(response)