Instructions to use clesterdpt/caredraft-e4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use clesterdpt/caredraft-e4b 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 clesterdpt/caredraft-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf clesterdpt/caredraft-e4b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf clesterdpt/caredraft-e4b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf clesterdpt/caredraft-e4b: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 clesterdpt/caredraft-e4b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf clesterdpt/caredraft-e4b: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 clesterdpt/caredraft-e4b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf clesterdpt/caredraft-e4b:Q4_K_M

Use Docker

docker model run hf.co/clesterdpt/caredraft-e4b:Q4_K_M

LM Studio
Jan
Ollama
How to use clesterdpt/caredraft-e4b with Ollama:
```
ollama run hf.co/clesterdpt/caredraft-e4b:Q4_K_M
```
Unsloth Desktop

How to use clesterdpt/caredraft-e4b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf clesterdpt/caredraft-e4b:Q4_K_M

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": "clesterdpt/caredraft-e4b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use clesterdpt/caredraft-e4b with Docker Model Runner:
```
docker model run hf.co/clesterdpt/caredraft-e4b:Q4_K_M
```

Lemonade

How to use clesterdpt/caredraft-e4b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull clesterdpt/caredraft-e4b:Q4_K_M

Run and chat with the model

lemonade run user.caredraft-e4b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use clesterdpt/caredraft-e4b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf clesterdpt/caredraft-e4b: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 clesterdpt/caredraft-e4b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use clesterdpt/caredraft-e4b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf clesterdpt/caredraft-e4b:Q4_K_M

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 "clesterdpt/caredraft-e4b:Q4_K_M" \
  --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"

CareDraft E4B (fine-tuned)

A LoRA fine-tune of Gemma 4 E4B QAT (google/gemma-4-E4B-it), trained by the CareDraft team to turn rough, speech-to-text home-health dictation into polished, Medicare-compliant clinical documentation across physical therapy, occupational therapy, speech-language pathology, and skilled nursing.

Training data is 100% synthetic — no real patient information, transcripts, or notes were used or are reproducible from this model. All training pairs were generated and mechanically validated (numeric-fact preservation, denial/ negation integrity, no unsupported claims) before training.

Format: GGUF, Q4_K_M quantization
Size: ~5.30 GB
SHA-256: 9e03f6af0b55fcb88358624055fa622b64a46b92803599b37e1016aa5eb011ca
Base: Gemma 4 E4B QAT (4-bit)
Intended use: On-device note generation inside the CareDraft app. Not intended for standalone medical use — all output requires clinician review before it becomes part of a medical record.

License

This model is a derivative of Google's Gemma models and is distributed under the Gemma Terms of Use. By downloading or using this model you agree to those terms, including the Gemma Prohibited Use Policy.

Usage

This model is built for CareDraft's local llama.cpp-based inference pipeline (Gemma chat template, <end_of_turn>/<eos> stop tokens) and is not packaged as a general-purpose chat assistant.

Downloads last month: 61

GGUF

Model size

7B params

Architecture

gemma4

Hardware compatibility

4-bit

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Model tree for clesterdpt/caredraft-e4b

Base model

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