Instructions to use clesterdpt/caredraft-e2b 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-e2b 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-e2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf clesterdpt/caredraft-e2b: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-e2b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf clesterdpt/caredraft-e2b: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-e2b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf clesterdpt/caredraft-e2b: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-e2b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf clesterdpt/caredraft-e2b:Q4_K_M

Use Docker

docker model run hf.co/clesterdpt/caredraft-e2b:Q4_K_M

LM Studio
Jan
Ollama
How to use clesterdpt/caredraft-e2b with Ollama:
```
ollama run hf.co/clesterdpt/caredraft-e2b:Q4_K_M
```
Unsloth Desktop

How to use clesterdpt/caredraft-e2b 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-e2b: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-e2b:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use clesterdpt/caredraft-e2b with Docker Model Runner:
```
docker model run hf.co/clesterdpt/caredraft-e2b:Q4_K_M
```

Lemonade

How to use clesterdpt/caredraft-e2b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull clesterdpt/caredraft-e2b:Q4_K_M

Run and chat with the model

lemonade run user.caredraft-e2b-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use clesterdpt/caredraft-e2b 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-e2b: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-e2b:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use clesterdpt/caredraft-e2b 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-e2b: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-e2b: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 E2B (fine-tuned)

A LoRA fine-tune of Gemma 4 E2B QAT (google/gemma-4-E2B-it), trained by the CareDraft team to turn rough, speech-to-text home-health dictation into polished draft 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: ~3.42 GB
SHA-256: 1f28939152a73032018c11f6ca484b8333ad4dbcc62333cf119129472cdab142
Base: Gemma 4 E2B 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 modified version of Google's Gemma 4 E2B, which is released under the Apache License 2.0. The fine-tuned weights are distributed under the same license; see LICENSE.

Modifications: CareDraft LLC fine-tuned the instruction-tuned Gemma 4 E2B QAT checkpoint with LoRA on synthetic home-health dictation, merged the adapter into the base weights, and converted and quantized the result to GGUF (Q4_K_M).

This model is not made or endorsed by Google.

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: 34

GGUF

Model size

5B params

Architecture

gemma4

Hardware compatibility

4-bit

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