Instructions to use majentik/garden with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/garden with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="majentik/garden")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("majentik/garden", device_map="auto")

MLX

How to use majentik/garden with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm
# if on a CUDA device, also pip install mlx[cuda]

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

model, tokenizer = load("majentik/garden")

prompt = "Once upon a time in"
text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use majentik/garden with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "majentik/garden"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/garden",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/majentik/garden

SGLang

How to use majentik/garden with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "majentik/garden" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/garden",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "majentik/garden" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "majentik/garden",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

MLX LM

How to use majentik/garden with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Generate some text
mlx_lm.generate --model "majentik/garden" --prompt "Once upon a time"

Docker Model Runner
How to use majentik/garden with Docker Model Runner:
```
docker model run hf.co/majentik/garden
```
Atomic Chat

majentik commited on Jul 9

Commit

fed0b9f

verified ·

1 Parent(s): c1c928b

docs: remove AWQ variants (AWQ lane paused; MLX focus)

Browse files

Files changed (1) hide show

README.md +0 -2

README.md CHANGED Viewed

@@ -62,13 +62,11 @@ repo specifies which):
   `llama.cpp`, `ollama`, `lmstudio`, or any GGUF-compatible runtime.
 - **MLX** — 2-bit, 4-bit, 8-bit. Targets Apple Silicon. Pip install
   `mlx-lm`, point it at the repo, done.
-- **AWQ** — 4-bit and 8-bit. Targets CUDA GPUs with vLLM or autoawq.
 ## Pick a starting point
 - **I have a MacBook with 16 GB RAM** → try a 4-bit MLX variant of
   Gemma 4 E4B.
-- **I have a 24 GB GPU** → try an AWQ-4bit Qwen3.5-27B with RotorQuant.
 - **I need 128k context on modest hardware** → any GGUF + RotorQuant.
 - **I want to compare** → each repo card links to the corresponding
   upstream model, so perplexity drift is one `eval_hf.py` run away.

   `llama.cpp`, `ollama`, `lmstudio`, or any GGUF-compatible runtime.
 - **MLX** — 2-bit, 4-bit, 8-bit. Targets Apple Silicon. Pip install
   `mlx-lm`, point it at the repo, done.
 ## Pick a starting point
 - **I have a MacBook with 16 GB RAM** → try a 4-bit MLX variant of
   Gemma 4 E4B.
 - **I need 128k context on modest hardware** → any GGUF + RotorQuant.
 - **I want to compare** → each repo card links to the corresponding
   upstream model, so perplexity drift is one `eval_hf.py` run away.