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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use datadab/writeamp-models 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 datadab/writeamp-models:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf datadab/writeamp-models:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/datadab/writeamp-models:Q4_K_M

LM Studio
Jan

vLLM

How to use datadab/writeamp-models with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/datadab/writeamp-models:Q4_K_M

Ollama
How to use datadab/writeamp-models with Ollama:
```
ollama run hf.co/datadab/writeamp-models:Q4_K_M
```

Unsloth Studio

How to use datadab/writeamp-models 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 datadab/writeamp-models 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 datadab/writeamp-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for datadab/writeamp-models to start chatting

How to use datadab/writeamp-models with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf datadab/writeamp-models: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": "datadab/writeamp-models:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use datadab/writeamp-models with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf datadab/writeamp-models: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 "datadab/writeamp-models: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"

Docker Model Runner
How to use datadab/writeamp-models with Docker Model Runner:
```
docker model run hf.co/datadab/writeamp-models:Q4_K_M
```

Lemonade

How to use datadab/writeamp-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull datadab/writeamp-models:Q4_K_M

Run and chat with the model

lemonade run user.writeamp-models-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use datadab/writeamp-models with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf datadab/writeamp-models: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 datadab/writeamp-models:Q4_K_M

Run Hermes

hermes

Atomic Chat

writeamp-models

File size: 2,750 Bytes

---
license: apache-2.0
tags:
  - text-generation
  - gguf
  - smollm2
  - qwen3
  - hinglish
  - mirror
---

# WriteAmp's on-device model catalog

This repository hosts WriteAmp's downloadable model artifacts. The catalog has three tiers
(`mini`, `midi`, `max`); two of them are mirrors of upstream Q4_K_M quantizations, one is a
custom Hinglish fine-tune.

| Tier | File | Size | SHA-256 | Source |
| --- | --- | --- | --- | --- |
| `mini` | [`mini/SmolLM2-135M.Q4_K_M.gguf`](resolve/main/mini/SmolLM2-135M.Q4_K_M.gguf?download=true) | 105,454,016 B | `127c52bae983b2d0de09c60d6292743e3239e44239bbe8ccb8cf3241513ff5a0` | Mirror of [`mradermacher/SmolLM2-135M-GGUF`](https://huggingface.co/mradermacher/SmolLM2-135M-GGUF) (Apache 2.0) |
| `midi` | [`midi/SmolLM2-360M.Q4_K_M.gguf`](resolve/main/midi/SmolLM2-360M.Q4_K_M.gguf?download=true) | 270,590,464 B | `4ac7aa712c43fe7d07ed910298b5eaab932292692954abd8e0703307724e4e04` | Mirror of [`mradermacher/SmolLM2-360M-GGUF`](https://huggingface.co/mradermacher/SmolLM2-360M-GGUF) (Apache 2.0) |
| `max` | [`WriteAmp-Qwen3-0.6B-Hinglish-v3.Q4_K_M.gguf`](resolve/main/WriteAmp-Qwen3-0.6B-Hinglish-v3.Q4_K_M.gguf?download=true) | 396,704,544 B | `98df7dd1daaf65602fa002733d5c5a32527acd174730b591235f0ed170f17185` | Custom QLoRA fine-tune of [`Qwen/Qwen3-0.6B-Base`](https://huggingface.co/Qwen/Qwen3-0.6B-Base) (Apache 2.0) |

Each tier's mirror card (under `mini/` and `midi/`) carries provenance and license details. The
`max` tier's full description, training data, and known limitations are in
[`WriteAmp-Qwen3-0.6B-Hinglish-v3.Q4_K_M.gguf`](resolve/main/WriteAmp-Qwen3-0.6B-Hinglish-v3.Q4_K_M.gguf?download=true)'s
card above.

## Mirroring policy

`mini` and `midi` are byte-identical mirrors of `mradermacher`'s community Q4_K_M quantizations.
The WriteAmp app's catalog keeps `mradermacher`'s URLs as the primary download path (the
community attribution is preserved), and uses this repo as a cold-start fallback when the primary
becomes unreachable or returns HTTP 404. See ADR-245 in the WriteAmp decision log for the full
rationale.

The `max` tier is hosted here as the primary (no third-party mirror). It is also the source of
truth for the `max` catalog slot.

## License

All artifacts under this repo are distributed under the **Apache License 2.0**, consistent with
their upstream base models (`HuggingFaceTB/SmolLM2-135M`, `HuggingFaceTB/SmolLM2-360M`,
`Qwen/Qwen3-0.6B-Base`) and `mradermacher`'s Q4_K_M quantizations of those bases.

## Citation

```
@misc{writeamp_models_2026,
  title  = {WriteAmp on-device model catalog: SmolLM2 mirrors + Qwen3-0.6B Hinglish fine-tune},
  author = {DataDab LLP},
  year   = {2026},
  url    = {https://huggingface.co/datadab/writeamp-models}
}
```